Big data collection practices and regulations
For many years, companies had few restrictions on the data they collected from their customers. However, as the collection and use of big data have increased, so has data misuse. Concerned citizens who have experienced the mishandling of their personal data or have been victims of a data breach are calling for laws around data collection transparency and consumer data privacy.

The outcry about personal privacy violations led the European Union to pass the General Data Protection Regulation (GDPR), which took effect in May 2018; it limits the types of data that organizations can collect and requires opt-in consent from individuals or compliance with other specified lawful grounds for collecting personal data. GDPR also includes a right-to-be-forgotten provision, which lets EU residents ask companies to delete their data.

While there aren't similar federal laws in the U.S., the California Consumer Privacy Act (CCPA) aims to give California residents more control over the collection and use of their personal information by companies. CCPA was signed into law in 2018 and is scheduled to take effect on Jan. 1, 2020. In addition, government officials in the U.S. are investigating data handling practices, specifically among companies that collect consumer data and sell it to other companies for unknown use.

The human side of big data analytics
Ultimately, the value and effectiveness of big data depend on the workers tasked with understanding the data and formulating the proper queries to direct big data analytics projects. Some big data tools meet specialized niches and enable less technical users to use everyday business data in predictive analytics applications. Other technologies -- such as Hadoop-based big data appliances -- help businesses implement a suitable compute infrastructure to tackle big data projects, while minimizing the need for hardware and distributed software know-how.

Big data can be contrasted with small data, another evolving term that's often used to describe data whose volume and format can be easily used for self-service analytics. A commonly quoted axiom is that "big data is for machines; small data is for people."

To improve service levels even further, public cloud providers offer big data capabilities through managed services that include the following:

Amazon EMR (formerly Elastic MapReduce)
Microsoft Azure HDInsight
Google Cloud Dataproc
In cloud environments, big data can be stored in the following:

Hadoop Distributed File System (HDFS);
lower-cost cloud object storage, such as Amazon Simple Storage Service (S3);
NoSQL databases; and
relational databases.
For organizations that want to deploy on-premises big data systems, commonly used Apache open source technologies in addition to Hadoop and Spark include the following:

YARN, Hadoop's built-in resource manager and job scheduler, which stands for Yet Another Resource Negotiator but is commonly known by the acronym alone;
the MapReduce programming framework, also a core component of Hadoop;
Kafka, an application-to-application messaging and data streaming platform;
the HBase database; and
SQL-on-Hadoop query engines, like Drill, Hive, Impala and Presto.
Users can install the open source versions of the technologies themselves or turn to commercial big data platforms offered by Cloudera, which merged with former rival Hortonworks in January 2019, or Hewlett Packard Enterprise (HPE), which bought the assets of big data vendor MapR Technologies in August 2019. The Cloudera and MapR platforms are also supported in the cloud.

Big data challenges
Besides the processing capacity and cost issues, designing a big data architecture is another common challenge for users. Big data systems must be tailored to an organization's particular needs, a DIY undertaking that requires IT teams and application developers to piece together a set of tools from all the available technologies. Deploying and managing big data systems also require new skills compared to the ones possessed by database administrators (DBAs) and developers focused on relational software.

Both of those issues can be eased by using a managed cloud service, but IT managers need to keep a close eye on cloud usage to make sure costs don't get out of hand. Also, migrating on-premises data sets and processing workloads to the cloud is often a complex process for organizations.

Making the data in big data systems accessible to data scientists and other analysts is also a challenge, especially in distributed environments that include a mix of different platforms and data stores. To help analysts find relevant data, IT and analytics teams are increasingly working to build data catalogs that incorporate metadata management and data lineage functions. Data quality and data governance also need to be priorities to ensure that sets of big data are clean, consistent and used properly.

Some data scientists also add value to the list of characteristics of big data. As explained above, not all data collected has real business value, and the use of inaccurate data can weaken the insights provided by analytics applications. It's critical that organizations employ practices such as data cleansing and confirm that data relates to relevant business issues before they use it in a big data analytics project.

Variability also often applies to sets of big data, which are less consistent than conventional transaction data and may have multiple meanings or be formatted in different ways from one data source to another -- factors that further complicate efforts to process and analyze the data. Some people ascribe even more Vs to big data; data scientists and consultants have created various lists with between seven and 10 Vs.

How big data is stored and processed
The need to handle big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate processing capacity to big data tasks in order to achieve the required velocity. This can potentially demand hundreds or thousands of servers that can distribute the processing work and operate collaboratively in a clustered architecture, often based on technologies like Hadoop and Apache Spark.

Achieving such velocity in a cost-effective manner is also a challenge. Many enterprise leaders are reticent to invest in an extensive server and storage infrastructure to support big data workloads, particularly ones that don't run 24/7. As a result, public cloud computing is now a primary vehicle for hosting big data systems. A public cloud provider can store petabytes of data and scale up the required number of servers just long enough to complete a big data analytics project. The business only pays for the storage and compute time actually used, and the cloud instances can be turned off until they're needed again.

Examples of big data
Big data comes from myriad different sources, such as business transaction systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, scientific research repositories, machine-generated data and real-time data sensors used in internet of things (IoT) environments. The data may be left in its raw form in big data systems or preprocessed using data mining tools or data preparation software so it's ready for particular analytics uses.

Using customer data as an example, the different branches of analytics that can be done with the information found in sets of big data include the following:

Comparative analysis. This includes the examination of user behavior metrics and the observation of real-time customer engagement in order to compare one company's products, services and brand authority with those of its competition.
Social media listening. This is information about what people are saying on social media about a specific business or product that goes beyond what can be delivered in a poll or survey. This data can be used to help identify target audiences for marketing campaigns by observing the activity surrounding specific topics across various sources.
Marketing analysis. This includes information that can be used to make the promotion of new products, services and initiatives more informed and innovative.
Customer satisfaction and sentiment analysis. All of the information gathered can reveal how customers are feeling about a company or brand, if any potential issues may arise, how brand loyalty might be preserved and how customer service efforts might be improved.
Breaking down the Vs of big data
Volume is the most commonly cited characteristic of big data. A big data environment doesn't have to contain a large amount of data, but most do because of the nature of the data being collected and stored in them. Clickstreams, system logs and stream processing systems are among the sources that typically produce massive volumes of big data on an ongoing basis

GOOD MORNING TO ALL ARBB FIGHTERS!

Big data

Posted by: Margaret Rouse
WhatIs.com

Contributor(s): Bridget Botelho, Stephen J. Bigelow
Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.

Systems that process and store big data have become a common component of data management architectures in organizations. Big data is often characterized by the 3Vs: the large volume of data in many environments, the wide variety of data types stored in big data systems and the velocity at which the data is generated, collected and processed. These characteristics were first identified by Doug Laney, then an analyst at Meta Group Inc., in 2001; Gartner further popularized them after it acquired Meta Group in 2005. More recently, several other Vs have been added to different descriptions of big data, including veracity, value and variability.

Although big data doesn't equate to any specific volume of data, big data deployments often involve terabytes (TB), petabytes (PB) and even exabytes (EB) of data captured over time.

Importance of big data
Companies use the big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences and, ultimately, increase profitability. Businesses that utilize big data hold a potential competitive advantage over those that don't since they're able to make faster and more informed business decisions, provided they use the data effectively.

For example, big data can provide companies with valuable insights into their customers that can be used to refine marketing campaigns and techniques in order to increase customer engagement and conversion rates.

Furthermore, utilizing big data enables companies to become increasingly customer-centric. Historical and real-time data can be used to assess the evolving preferences of consumers, consequently enabling businesses to update and improve their marketing strategies and become more responsive to customer desires and needs.

Big data is also used by medical researchers to identify disease risk factors and by doctors to help diagnose illnesses and conditions in individual patients. In addition, data derived from electronic health records (EHRs), social media, the web and other sources provides healthcare organizations and government agencies with up-to-the-minute information on infectious disease threats or outbreaks.

In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids. Financial services firms use big data systems for risk management and real-time analysis of market data. Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes. Other government uses include emergency response, crime prevention and smart city initiatives.

Big data also encompasses a wide variety of data types, including the following:

structured data in databases and data warehouses based on Structured Query Language (SQL);
unstructured data, such as text and document files held in Hadoop clusters or NoSQL database systems; and
semistructured data, such as web server logs or streaming data from sensors.
All of the various data types can be stored together in a data lake, which typically is based on Hadoop or a cloud object storage service. In addition, big data applications often include multiple data sources that may not otherwise be integrated. For example, a big data analytics project may attempt to gauge a product's success and future sales by correlating past sales data, return data and online buyer review data for that product.

Velocity refers to the speed at which big data is generated and must be processed and analyzed. In many cases, sets of big data are updated on a real- or near-real-time basis, instead of the daily, weekly or monthly updates made in many traditional data warehouses. Big data analytics applications ingest, correlate and analyze the incoming data and then render an answer or result based on an overarching query. This means data scientists and other data analysts must have a detailed understanding of the available data and possess some sense of what answers they're looking for to make sure the information they get is valid and up to date.

Managing data velocity is also important as big data analysis expands into fields like machine learning and artificial intelligence (AI), where analytical processes automatically find patterns in the collected data and use them to generate insights.

More characteristics of big data
Looking beyond the original 3Vs, data veracity refers to the degree of certainty in data sets. Uncertain raw data collected from multiple sources -- such as social media platforms and webpages -- can cause serious data quality issues that may be difficult to pinpoint. For example, a company that collects sets of big data from hundreds of sources may be able to identify inaccurate data, but its analysts need data lineage information to trace where the data is stored so they can correct the issues.

Bad data leads to inaccurate analysis and may undermine the value of business analytics because it can cause executives to mistrust data as a whole. The amount of uncertain data in an organization must be accounted for before it is used in big data analytics applications. IT and analytics teams also need to ensure that they have enough accurate data available to produce valid results.

good night to all arbb fighters!

Keep speculating good investors of Arbb7181...

FYI, beside KLSE, I have also invested in Technology Stocks in Nasdaq. One of them has 40% of World 5G Deployment across the Globe.

For KLSE, I have 6 Boutiques
Tech ir4.0 industry
Construction
Plantation
O&G
Digital Economy
Visit Malaysia Year.

All these companies under these 6 Boutiques give me the pulse of our economy. Current performance in term of margin:

P1 - Plantation
P2 - Oil & Gas
P3 - Ir4.0 industry
P4 - Construction
P5 - Digital Economy
P6 - Visit Malaysia Year 2020

It’s just like an F1 Race between Mercedes Benz, Ferrari, Red Bulls and Maclaren.

yes agree with 靓女 phoon phoon

when arb shares price goes down to 0.23 i clean up rm100,000, then next 4days i sell all at 0.315.can both of you clowns count how much i earn from arbb shares? oh i forgot, you 2 cheapskate are very poor kids, jobless kids or maybe still need parents support? still using public mrt to work or doing small business? i just laugh all the way to the bank and now its sapu AGESON7145 ICPS NOW GUYS!

hahaha dumbo jumbo small kids crying all day long for father mother

yes your RM100mil revenue and 34mil profit from? So far, out of 34mil profit before tax, got 59mil in form of trade receivables, about RM10mil carried forward from last year, an increased of 49mil. PBT for 2019 (5076+8612+8236+12683) = RM 34607.

ARBB's financial statement can't balance le. ARBB whole year PBT only 34.6mil. Assuming you collect zero cash, how to increase trade receivables by 49mil with 34.6mil PBT is totally fake post! you work in arbb? what post are you rj87? i as an investor might go to arbb office to look for you and ask you right in your face!

Advantages and Disadvantages of Big Data
The increase in the amount of data available presents both opportunities and problems.

In general, having more data on one’s customers (and potential customers) should allow companies to better tailor their products and marketing efforts in order to create the highest level of satisfaction and repeat business. Companies that are able to collect a large amount of data are provided with the opportunity to conduct deeper and richer analysis.

While better analysis is a positive, big data can also create overload and noise. Companies have to be able to handle larger volumes of data, all the while determining which data represents signals compared to noise. Determining what makes the data relevant becomes a key factor.

Furthermore, the nature and format of the data can require special handling before it is acted upon. Structured data, consisting of numeric values, can be easily stored and sorted. Unstructured data, such as emails, videos, and text documents, may require more sophisticated techniques to be applied before it becomes useful.

Big Data

What Is Big Data?
Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. Big data often comes from multiple sources and arrives in multiple formats.

How Big Data Works
Big data can be categorized as unstructured or structured. Structured data consists of information already managed by the organization in databases and spreadsheets; it is frequently numeric in nature. Unstructured data is information that is unorganized and does not fall into a pre-determined model or format. It includes data gathered from social media sources, which help institutions gather information on customer needs.

Three Vs traditionally characterize big data: the volume (amount) of data, the velocity (speed) at which it is collected, and the variety of the info.
Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances.

Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets. Many software-as-a-service (SaaS) companies specialize in managing this type of complex data.

The Uses of Big Data
Data analysts look at the relationship between different types of data, such as demographic data and purchase history, to determine whether a correlation exists. Such assessments may be done in-house within a company or externally by a third-party who focuses on processing big data into digestible formats. Businesses often use the assessment of big data by such experts to turn it into actionable information.

Nearly every department in a company can utilize findings from data analysis, from human resources and technology to marketing and sales. The goal of big data is to increase the speed at which products get to market, to reduce the amount of time and resources required to gain market adoption, target audiences, and to ensure that customers remain satisfied.

KEY TAKEAWAYS
Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.
Big data can be structured (often numeric, easily formatted, and stored) or unstructured (more free-form, less quantifiable).
Nearly every department in a company can utilize findings from big data analysis, but handling its clutter and noise can pose problems.

wow morning to both of you and to all arbb7181 fighters!
looking good today

Industry 4.0 applications today

While many organizations might still be in denial about how Industry 4.0 could impact their business or struggling to find the talent or knowledge to know how to best adopt it for their unique use cases, several others are implementing changes today and preparing for a future where smart machines improve their business. Here are just a few of the possible applications:

Identify opportunities: Since connected machines collect a tremendous volume of data that can inform maintenance, performance and other issues, as well as analyze that data to identify patterns and insights that would be impossible for a human to do in a reasonable timeframe, Industry 4.0 offers the opportunity for manufacturers to optimize their operations quickly and efficiently by knowing what needs attention. By using the data from sensors in its equipment, an African gold mine identified a problem with the oxygen levels during leaching. Once fixed, they were able to increase their yield by 3.7%, which saved them $20 million annually.

Optimize logistics and supply chains: A connected supply chain can adjust and accommodate when new information is presented. If a weather delay ties up a shipment, a connected system can proactively adjust to that reality and modify manufacturing priorities.

Autonomous equipment and vehicles: There are shipping yards that are leveraging autonomous cranes and trucks to streamline operations as they accept shipping containers from the ships.

Robots: Once only possible for large enterprises with equally large budgets, robotics are now more affordable and available to organizations of every size. From picking products at a warehouse to getting them ready to ship, autonomous robots can quickly and safely support manufacturers. Robots move goods around Amazon warehouses and also reduce costs and allow better use of floor space for the online retailer.

Additive manufacturing (3D printing): This technology has improved tremendously in the last decade and has progressed from primarily being used for prototyping to actual production. Advances in the use of metal additive manufacturing have opened up a lot of possibilities for production.

Internet of Things and the cloud: A key component of Industry 4.0 is the Internet of Things that is characterized by connected devices. Not only does this help internal operations, but through the use of the cloud environment where data is stored, equipment and operations can be optimized by leveraging the insights of others using the same equipment or to allow smaller enterprises access to technology they wouldn’t be able to on their own.

While Industry 4.0 is still evolving and we might not have the complete picture until we look back 30 years from now, companies who are adopting the technologies realize Industry 4.0's potential. These same companies are also grappling with how to upskill their current workforce to take on new work responsibilities made possible by Internet 4.0 and to recruit new employees with the right skills.

GOOD MORNING TO ALL ARBB FIGHTERS!

What is Industry 4.0? Here's A Super Easy Explanation For Anyone

We’re in the midst of a significant transformation regarding the way we produce products thanks to the digitization of manufacturing. This transition is so compelling that it is being called Industry 4.0 to represent the fourth revolution that has occurred in manufacturing. From the first industrial revolution (mechanization through water and steam power) to the mass production and assembly lines using electricity in the second, the fourth industrial revolution will take what was started in the third with the adoption of computers and automation and enhance it with smart and autonomous systems fueled by data and machine learning.

Even though some dismiss Industry 4.0 as merely a marketing buzzword, shifts are happening in manufacturing that deserves our attention.

Industry 4.0 optimizes the computerization of Industry 3.0

When computers were introduced in Industry 3.0, it was disruptive thanks to the addition of an entirely new technology. Now, and into the future as Industry 4.0 unfolds, computers are connected and communicate with one another to ultimately make decisions without human involvement. A combination of cyber-physical systems, the Internet of Things and the Internet of Systems make Industry 4.0 possible and the smart factory a reality. As a result of the support of smart machines that keep getting smarter as they get access to more data, our factories will become more efficient and productive and less wasteful. Ultimately, it's the network of these machines that are digitally connected with one another and create and share information that results in the true power of Industry 4.0.

Natural language Data: human-generated data, particularly in the verbal form. Such data differ in terms of the level of abstraction and level of editorial quality. The sources of natural language data include speech capture devices, land phones, mobile phones, and Internet of Things that generate large sizes of text-like communication between devices.
Time series: a sequence of data points (or observations), typically consisting of successive measurements made over a time interval. The goal is to detect trends and anomalies, identify context and external influences, and compare individual against the group or compare individual at different times. There are two kinds of time series data: (i) continuous, where we have an observation at every instant of time and (ii) where we have an observation at (usually regularly) spaced intervals. Examples of such data include ocean tides, counts of sunspots, the daily closing value of the Dow Jones Industrial Average, and measuring the level of unemployment each month of the year.
Event data: data generated from the matching between external events with time series. This requires the identification of important events from the unimportant. For example, information related to vehicle crashes or accidents can be collected and analyzed to help understand what the vehicles were doing before, during and after the event. The data in this example is generated by sensors fixed in different places of the vehicle body. Event data consists of three mains pieces of information: (i) action, which is the event itself, (ii) timestamp, the time when this event happened, and (iii) state, which describes all other information relevant to this event. Event data is usually described as rich, denormalized, nested and schemaless.
Network data: data concerns very large networks, such as social networks (e.g. Facebook and Twitter), information networks (e.g. the World Wide Web), biological networks (e.g. biochemical, ecological and neural networks), and technological networks (e.g. the Internet, telephone and transportation networks). Network data is represented as nodes connected via one or more types of relationship. In social networks, nodes typically represent people. In information networks, nodes represent data items (e.g. webpages). In technological networks, nodes may represent Internet devices (e.g. routers and hubs) or telephone switches. In biological networks, nodes may represent neural cells. Much of the interesting work here is on network structure and connections between network nodes.
Linked data: data that is built upon standard Web technologies such as HTTP, RDF, SPARQL and URIs to share information that can be semantically queried by computers (rather than serving human needs). This allows data from different sources to be connected and read. The term was coined by Tim Berners-Lee, director of the World Wide Web Consortium, in a design note about the Semantic Web project. This project allowed the Web to connect related data that wasn’t linked in the past by providing the mechanisms and lowering the barriers to linking data currently linked. Examples of repositories for linked data include (i) DBpedia, a dataset containing extracted data from Wikipedia, (ii) GeoNames, RDF descriptions of more than 7,500,000 geographical features worldwide, (iii) UMBEL, a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc, and (iv) FOAF, friend of a friend, a dataset describing persons, their properties and relationships. Linked open data is another project that targets linked data with open content.
Finally, each data type has different requirements for analysis and poses different challenges. In principle, the interpretation of data is known but in practice, nobody has the full picture.

GOODDAY TO ALL ARBB FIGHTERS!

Big Data: Types of Data Used in Analytics

Data types involved in Big Data analytics are many: structured, unstructured, geographic, real-time media, natural language, time series, event, network and linked. It is necessary here to distinguish between human-generated data and device-generated data since human data is often less trustworthy, noisy and unclean.
Structured data: data stored in rows and columns, mostly numerical, where the meaning of each data item is defined. This type of data constitutes about 10% of the today’s total data and is accessible through database management systems. Example sources of structured (or traditional) data include official registers that are created by governmental institutions to store data on individuals, enterprises and real estates; and sensors in industries that collect data about the processes. Today, sensor data is one of the fast growing areas, particularly that sensors are installed in plants to monitor movement, temperature, location, light, vibration, pressure, liquid and flow.
Unstructured data: data of different forms like e.g. text, image, video, document, etc. It can also be in the form of customer complaints, contracts, or internal emails. This type of data accounts for about 90% of the data created in this century. In fact, the volcanic growth of social media (e.g. Facebook and Twitter), since the middle of the last decade, is responsible for the major part of the unstructured data that we have today. Unstructured data cannot be stored using traditional relational databases. Storing data with such a variety and complexity requires the use of adequate storage systems, commonly referred to as NoSQL databases, like e.g. MongoDB and CouchDB. The importance of unstructured data is located in the embedded interrelationships that may not be discovered if other types of data are considered. What makes data generated in social media different from other types of data is that data in social media has a personal taste.
Geographic data: data related to roads, buildings, lakes, addresses, people, workplaces, and transportation routes, that are generated from geographic information systems. These data link between place, time, and attributes (i.e. descriptive information). Geographic data, which is digital, have huge benefits over traditional data sources such as maps, such as paper maps, written reports from explorers, and spoken accounts in that digital data are easy to copy, store, and transmit. More importantly, they are easy to transform, process, and analyze. Such data is useful in urban planning and for monitoring environmental effects. A branch of statistics that is involved in spatial or spatiotemporal data is called Geostatistics.
Real-time media: real-time streaming of live or stored media data. A special characteristic of real-time media is the amount of data being produced which will be more confusing in the future in terms of storage and processing. One of the main sources of media data is services like e.g. YouTube, Flicker, and Vimeo that produce a huge amount of video, pictures, and audio. Another important source or real-time media is video conferencing (or visual collaboration) which allow two or more locations to communicate simultaneously in two-way video and audio transmission.

wow bettertomolo you work inside arb? i dont think so

Influenza Vaccine Viruses
One influenza A(H1N1), one influenza A(H3N2), and one or two influenza B viruses (depending on the vaccine) are included in each season’s influenza vaccines. Getting a flu vaccine can protect against flu viruses that are like the viruses used to make vaccine. Information about this season’s vaccine can be found at Preventing Seasonal Flu with Vaccination. Seasonal flu vaccines do not protect against influenza C or D viruses. In addition, flu vaccines will NOT protect against infection and illness caused by other viruses that also can cause influenza-like symptoms. There are many other viruses besides influenza that can result in influenza-like illness (ILI) that spread during flu season.

Clades and sub-clades can be alternatively called “groups” and “sub-groups,” respectively. An influenza clade or group is a further subdivision of influenza viruses (beyond subtypes or lineages) based on the similarity of their HA gene sequences. (See the Genome Sequencing and Genetic Characterization page for more information). Clades and subclades are shown on phylogenetic trees as groups of viruses that usually have similar genetic changes (i.e., nucleotide or amino acid changes) and have a single common ancestor represented as a node in the tree (see Figure 1). Dividing viruses into clades and subclades allows flu experts to track the proportion of viruses from different clades in circulation.

Note that clades and sub-clades that are genetically different from others are not necessarily antigenically different (i.e., viruses from a specific clade or sub-clade may not have changes that impact host immunity in comparison to other clades or sub-clades).

Currently circulating influenza A(H1N1) viruses are related to the pandemic 2009 H1N1 virus that emerged in the spring of 2009 and caused a flu pandemic (CDC 2009 H1N1 Flu website). This virus, scientifically called the “A(H1N1)pdm09 virus,” and more generally called “2009 H1N1,” has continued to circulate seasonally since then. These H1N1 viruses have undergone relatively small genetic changes and changes to their antigenic properties (i.e., the properties of the virus that affect immunity) over time.

Of all the influenza viruses that routinely circulate and cause illness in people, influenza A(H3N2) viruses tend to change more rapidly, both genetically and antigenically. Influenza A(H3N2) viruses have formed many separate, genetically different clades in recent years that continue to co-circulate.

Influenza B viruses are not divided into subtypes, but instead are further classified into two lineages: B/Yamagata and B/Victoria. Similar to influenza A viruses, influenza B viruses can then be further classified into specific clades and sub-clades. Influenza B viruses generally change more slowly in terms of their genetic and antigenic properties than influenza A viruses, especially influenza A(H3N2) viruses. Influenza surveillance data from recent years shows co-circulation of influenza B viruses from both lineages in the United States and around the world. However, the proportion of influenza B viruses from each lineage that circulate can vary by geographic location.

Understanding Influenza Viruses

There are four types of influenza viruses: A, B, C and D. Human influenza A and B viruses cause seasonal epidemics of disease (known as the flu season) almost every winter in the United States. Influenza A viruses are the only influenza viruses known to cause flu pandemics, i.e., global epidemics of flu disease. A pandemic can occur when a new and very different influenza A virus emerges that both infects people and has the ability to spread efficiently between people. Influenza type C infections generally cause mild illness and are not thought to cause human flu epidemics. Influenza D viruses primarily affect cattle and are not known to infect or cause illness in people.

Influenza A viruses are divided into subtypes based on two proteins on the surface of the virus: hemagglutinin (H) and neuraminidase (N). There are 18 different hemagglutinin subtypes and 11 different neuraminidase subtypes (H1 through H18 and N1 through N11, respectively). While there are potentially 198 different influenza A subtype combinations, only 131 subtypes have been detected in nature. Current subtypes of influenza A viruses that routinely circulate in people include: A(H1N1) and A(H3N2). Influenza A subtypes can be further broken down into different genetic “clades” and “sub-clades.” See the “Influenza Viruses” graphic below for a visual depiction of these classifications.

GOOD MORNING KENNYCHUA AND ALL ARBB 7181 FIGHTERS! GET READY GUYS!

Big Dataworks amongst top leading Private Companies in Shared Prosperity Vision 2030

In 2019 the Prime Minister, Tun Dr Mahathir Mohamad launched Shared Prosperity Vision 2030 as an effort to make Malaysia a country that is developing sustainably with fair economic distribution as well as equitable growth at all levels of incomes, ethnics, regions and supply chains by 2030.


The Shared Prosperity Vision 2030 which is the primary strategic policy document that will guide the restructuring priorities for Malaysia’s development was used as a baseline to identify the technology companies at the cutting edge of empowering or delivering shared prosperity in Malaysia. To make the list, companies needed to demonstrated that they were able to participate in a meaningful manner towards the shared prosperity vision enablers, namely effective institutional delivery, financial capital, governance & integrity especially related to increasing transparency, and lastly was the use of Big Data.



Big Dataworks is a Malaysian grown tech company providing solutions in data supply, data analytics, management of information and physical documents storage. The innovative approach to data driven business solutions has made it possible to manage a nationwide user base of over 200,000. The nimble solution provider has delivered higher productivity, better value for consumers, and the next wave of growth in the business through big data solutions to a wide range of stakeholders. The ability delivering directly with to the public, government, government linked corporations and the private sector has ensured that Big Dataworks makes this year’s list.

Real-time media: real-time streaming of live or stored media data. A special characteristic of real-time media is the amount of data being produced which will be more confusing in the future in terms of storage and processing. One of the main sources of media data is services like e.g. YouTube, Flicker, and Vimeo that produce a huge amount of video, pictures, and audio. Another important source or real-time media is video conferencing (or visual collaboration) which allow two or more locations to communicate simultaneously in two-way video and audio transmission.
Natural language Data: human-generated data, particularly in the verbal form. Such data differ in terms of the level of abstraction and level of editorial quality. The sources of natural language data include speech capture devices, land phones, mobile phones, and Internet of Things that generate large sizes of text-like communication between devices.
Time series: a sequence of data points (or observations), typically consisting of successive measurements made over a time interval. The goal is to detect trends and anomalies, identify context and external influences, and compare individual against the group or compare individual at different times. There are two kinds of time series data: (i) continuous, where we have an observation at every instant of time and (ii) where we have an observation at (usually regularly) spaced intervals. Examples of such data include ocean tides, counts of sunspots, the daily closing value of the Dow Jones Industrial Average, and measuring the level of unemployment each month of the year.
Event data: data generated from the matching between external events with time series. This requires the identification of important events from the unimportant. For example, information related to vehicle crashes or accidents can be collected and analyzed to help understand what the vehicles were doing before, during and after the event. The data in this example is generated by sensors fixed in different places of the vehicle body. Event data consists of three mains pieces of information: (i) action, which is the event itself, (ii) timestamp, the time when this event happened, and (iii) state, which describes all other information relevant to this event. Event data is usually described as rich, denormalized, nested and schemaless.
Network data: data concerns very large networks, such as social networks (e.g. Facebook and Twitter), information networks (e.g. the World Wide Web), biological networks (e.g. biochemical, ecological and neural networks), and technological networks (e.g. the Internet, telephone and transportation networks). Network data is represented as nodes connected via one or more types of relationship. In social networks, nodes typically represent people. In information networks, nodes represent data items (e.g. webpages). In technological networks, nodes may represent Internet devices (e.g. routers and hubs) or telephone switches. In biological networks, nodes may represent neural cells. Much of the interesting work here is on network structure and connections between network nodes.
Linked data: data that is built upon standard Web technologies such as HTTP, RDF, SPARQL and URIs to share information that can be semantically queried by computers (rather than serving human needs). This allows data from different sources to be connected and read. The term was coined by Tim Berners-Lee, director of the World Wide Web Consortium, in a design note about the Semantic Web project. This project allowed the Web to connect related data that wasn’t linked in the past by providing the mechanisms and lowering the barriers to linking data currently linked. Examples of repositories for linked data include (i) DBpedia, a dataset containing extracted data from Wikipedia, (ii) GeoNames, RDF descriptions of more than 7,500,000 geographical features worldwide, (iii) UMBEL, a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc, and (iv) FOAF, friend of a friend, a dataset describing persons, their properties and relationships. Linked open data is another project that targets linked data with open content.

GOOD MORNING TO ALL ARBB 7181 FIGHTERS!

Big Data: Types of Data Used in Analytics

Data types involved in Big Data analytics are many: structured, unstructured, geographic, real-time media, natural language, time series, event, network and linked. It is necessary here to distinguish between human-generated data and device-generated data since human data is often less trustworthy, noisy and unclean.
Structured data: data stored in rows and columns, mostly numerical, where the meaning of each data item is defined. This type of data constitutes about 10% of the today’s total data and is accessible through database management systems. Example sources of structured (or traditional) data include official registers that are created by governmental institutions to store data on individuals, enterprises and real estates; and sensors in industries that collect data about the processes. Today, sensor data is one of the fast growing areas, particularly that sensors are installed in plants to monitor movement, temperature, location, light, vibration, pressure, liquid and flow.
Unstructured data: data of different forms like e.g. text, image, video, document, etc. It can also be in the form of customer complaints, contracts, or internal emails. This type of data accounts for about 90% of the data created in this century. In fact, the volcanic growth of social media (e.g. Facebook and Twitter), since the middle of the last decade, is responsible for the major part of the unstructured data that we have today. Unstructured data cannot be stored using traditional relational databases. Storing data with such a variety and complexity requires the use of adequate storage systems, commonly referred to as NoSQL databases, like e.g. MongoDB and CouchDB. The importance of unstructured data is located in the embedded interrelationships that may not be discovered if other types of data are considered. What makes data generated in social media different from other types of data is that data in social media has a personal taste.
Geographic data: data related to roads, buildings, lakes, addresses, people, workplaces, and transportation routes, that are generated from geographic information systems. These data link between place, time, and attributes (i.e. descriptive information). Geographic data, which is digital, have huge benefits over traditional data sources such as maps, such as paper maps, written reports from explorers, and spoken accounts in that digital data are easy to copy, store, and transmit. More importantly, they are easy to transform, process, and analyze. Such data is useful in urban planning and for monitoring environmental effects. A branch of statistics that is involved in spatial or spatiotemporal data is called Geostatistics.

Becoming an Industry 4.0 Manufacturer

“Malaysia giant ARBB used the latter in an ERP SYSTEM. A computer-based system mimics the actions of an “ideal” operator, using real-time metrics to adjust and monitor position. The company found that the new tools boosted throughput by up to 5 percent.” – Arbb Berhad

Keeping up with the fast-paced, revolutionary world of Industry 4.0, IIoT, renewable energy and smart homes is imperative for today’s manufacturers and construction owners want to become tomorrow’s industry leaders.

Contact Arbb to implement their newest erp system, our unique Industry 4.0 solution.

GOOD MORNING TO ALL ARBB 7181 FIGHTERS!

What is Industry 4.0 and Why is it Important?

We’re in the midst of a significant transformation regarding the way we produce products thanks to the digitization of manufacturing. This compelling transition is called Industry 4.0 – which is a representation of the fourth revolution that has occurred in manufacturing. Like the three industrial revolutions which preceded it – steam power, mass production/electricity, digital age – Industry 4.0 will transform local and global economies and create a new future for us all.

Industry 4.0 Applications Today

While many organizations might still be in denial about Industry 4.0 and how it can impact their operations, many others have already started implementation and preparing for the future so that smart machines can improve their business.

Here are a Few Possible Applications

Connected machines collect a tremendous volume of data that can inform quality performance and many other issues as well as analyze data to identify patterns and insights that would be impossible for a human to do in a reasonable timeframe. Industry 4.0 gives manufacturers the ability for quick optimization, allowing efficiency by knowing what needs attention. By using data from sensors in its equipment, an African gold mine identified problems with the oxygen levels during leaching. Once fixed they were able to increase their yield by 3.7%, which saved them $20 million annually, according to McKinsey & Company.

Taking Industry 4.0 to a Whole New Level

Let’s take a watch for example: Before IoT, a watch was just a simple device that told time. Now we have smart watches, like the Apple Watch, that are connected to the Internet and have multiple sensors, tracking fitness and collecting other health-oriented data for us to use. IoT is redefining how people interact with objects and how they manage day-to-day tasks.

Industry 4.0 takes this concept to a whole new level – the Industrial Internet of Things (IIoT) and cyber-physical systems. Connecting the factory floor to IIoT software allows humans and machines to communicate with one another and work as one unified team. With Industry 4.0 manufacturers can gather real-time data from all parts of the manufacturing process so decisions can be made quickly and efficiently; and automate and streamline processes, reducing waste and increasing productivity and Overall Equipment Effectiveness (OEE).

The massive amount of accurate data that is obtained in real time from IIoT-outfitted machinery creates an evidence-based environment, allowing manufacturers to make informed decisions more confidently and quickly.

These insights are paramount to successful manufacturing in a global economy. Tracking production from start to finish ensures that all raw materials and finished goods meet regulatory standards and are of the highest quality. If a problem arises, the source can be discovered in near real-time, reducing material waste and the risk of catastrophic recalls.

Adding Machine Learning and Artificial Intelligence to the Mix

Predictive analytics, machine learning and artificial intelligence (AI) are also changing how many businesses and industries operate, including manufacturing. By using machine learning techniques and AI, developers can create prediction models based on data from systems with IIoT.

Algorithmic solutions can benefit manufacturers by predicting the quality of their finished goods at the start of the production process, in order to increase the chances of producing a Golden Batch EVERY time.

Data can be analyzed to predict how well machinery is running at all times. Problems with machinery can then be detected and fixed much faster, leading to fewer stoppages and increased output. The activities of the operators and supervisors on each production line can also be taken into account, their output measured and predicted, leading to increased productivity and a more efficient factory floor.

Prevention & Treatment

There is currently no vaccine to prevent 2019-nCoV infection. The best way to prevent infection is to avoid being exposed to this virus. However, as a reminder, CDC always recommends everyday preventive actions to help prevent the spread of respiratory viruses, including:

Avoid close contact with people who are sick.
Avoid touching your eyes, nose, and mouth with unwashed hands.
Stay home when you are sick.
Cover your cough or sneeze with a tissue, then throw the tissue in the trash.
Clean and disinfect frequently touched objects and surfaces using a regular household cleaning spray or wipe.
Follow CDC’s recommendations for using facemask.
CDC does not recommend that people who are well wear facemask to protect themselves from respiratory viruses, including 2019-nCoV.
Facemask should be used by people who show symptoms of 2019 novel coronavirus, in order to protect others from the risk of getting infected. The use of facemasks is also crucial for health workers and people who are taking care of someone in close settings (at home or in a health care facility).
Wash your hands often with soap and water for at least 20 seconds, especially after going to the bathroom; before eating; and after blowing your nose, coughing, or sneezing.
If soap and water are not readily available, use an alcohol-based hand sanitizer with at least 60% alcohol. Always wash hands with soap and water if hands are visibly dirty.
For information about handwashing, see CDC’s Handwashing website

For information specific to healthcare, see CDC’s Hand Hygiene in Healthcare Settings

These are everyday habits that can help prevent the spread of several viruses. CDC does have specific guidance for travelers.

GOOD MORNING TO ALL ARBB 7181 FIGHTERS!

2019 Novel Coronavirus

Symptoms

For confirmed 2019-nCoV infections, reported illnesses have ranged from people with mild symptoms to people being severely ill and dying. Symptoms can include:

Fever
Cough
Shortness of breath
CDC believes at this time that symptoms of 2019-nCoV may appear in as few as 2 days or as long as 14 after exposure. This is based on what has been seen previously as the incubation period of MERS viruses.

GOOD MORNING ELAINETAN, YES .ITS TIME TO SAPU ALL ARBB7181 SHARES NOW!

APU nurtures creativity and innovation while encouraging cross-cultural communication, allowing students to be equipped with the necessary technical and soft skills to be highly employable.

Such international exposures ensure APU graduates are open to differences in opinions, and they are also well-trained to engage in global conversations, international strategies, and multicultural encounters. In essence, they become confident and are highly employable.

All APU students also go through industry-advised, strategically designed curriculums that stress on innovation and advanced digital technologies and automation, readying them for complex, dialectical opportunities once they graduate.

To ensure its graduates are professional and highly employable, APU builds students’ confidence through practical workplace skills within the curriculum. Eventually, once they graduate, they are workplace-ready – a fact that is demonstrated by its students who are professionally attired, even on-campus.

To-date, APU graduates have impressed over 10,000 industrial partners and potential employers, and over 40,000 alumni are employed globally in reputable multinational companies such as Accenture, HP, IBM, Huawei Technologies, Astro, Maybank, Standard Chartered and more.

Building the IR4.0 Workforce

A university plays an important role in future-proofing school leavers and transforming them into industry-ready graduates in collaboration with the industry.

Through APU’s industry-academia partnership with various companies, the university can identify, recognise and mitigate any risk that IR4.0 may pose for its graduates, ensuring they stay competitive against the tide of increasing automation and machines.

To do that, APU formalises ties with various key industry players, in a partnership that involves an entire ecosystem of academic content development and delivery.

This includes regular programme reviews, joint certification and open internship and job opportunities for APU students.

Some of APU’s significant industry partners include:

IBM, which APU collaborated with to deliver a series of technical workshops, technology talks, industry visits, and more. This received overwhelming participation from APU students. The university has so far produced over 200 students as IBM certified solution designers and application developers.

SAS, which endorses APU’s undergraduate and postgraduate level programmes in data science by providing tools and educational material support for learning and research purposes. All APU data science graduates receive a Joint Professional Certificate from SAS upon completion.

Microsoft, which has been an APU industrial partner for over two decades. APU is one of the frontier universities on the Microsoft Talent Development programme. APU students engage directly with Microsoft professionals through workshops and talk sessions and many of them also attained professional Microsoft certification, which allows for greater job prospects. APU has also received the Microsoft Azure Educator Grant Award.

APU students have also won national- and international-level competitions organised by major industry players, such as FAMELab, Intel-CREST Industry-University Challenge, NASA Space Apps Challenge, World Asian Business Case Competition, SAS FinTech Challenge and more.

Students at APU are fully-prepared to join the future global workforce with confidence, not just for their first jobs, but for lifelong careers.

The framework covers IoT, data science, cyber security, cloud computing, AI or intelligent systems, mechatronics, e-business, digital marketing, financial technology (fintech) and mobile technology.

The university also observes the policies outlined in MITI’s Industry4WRD. These key areas are also developed to address the need for talents to transform Malaysia’s Digital Economy. The Industry4WRD policies focus on developing and implementing the right technological infrastructure to allow industries to undergo seamless digital transformation processes.

As an education institution, teaching and learning infrastructure and facilities within APU provide students a comfortable ecosystem for development, where they also receive instruction on the Nine Pillars.

The university is equipped with world-class infrastructures from France, Singapore, Germany, the United Kingdom, among others that allow students to gain hands-on experience, and exposure to real-time data and scenarios – to emerge as competent technology professionals under a real-world environment.

Once such facility at APU is the Cyber Security Talent Zone (CSTZ), which is also Malaysia’s first integrated cyber security talent zone. It houses a military-grade real-time cyber security monitoring software at the full-fledged Cyber Threats Simulation and Response Centre and Security Operations Centre, where students gain real-life exposure and practical experience.

APU nurtures creativity and innovation through its discussion spaces, think tanks, incubation zones and Innovation Labs on its Technology Park Malaysia (TPM), Bukit Jalil campus – which provides an out-of-classroom environment for students.

While its research centres provide students fruits for thought in robotics engineering, IoT, data analytics, forensic and cyber security, business digitisation and innovation, entrepreneurship and leadership.

APU also provides professional transformation by providing platforms for students to formulate world-changing ideas and develop innovative solutions for complex problems faced by industries undergoing digital transformation.

Moulding Job-ready Individuals

One crucial element that can never be replaced by increased mechanisation is of course the human touch. This takes the form of soft skills that are always welcomed by customers in any industry, and hence are qualities that employers look out for.

Students who graduate with this added potential are deemed industry-ready by most employers as they leave their academic institutions. In fact, surveys show that employers value these soft skills, which include communication skills, problem-solving skills, openness to learn and to gain new knowledge.

Such qualities are inculcated at APU for these skills are constantly nurtured on a day-to-day basis.

In addition, the university’s campus environment is home to international students from over 120 countries, offering local students the chance to mingle with a global community.

This is important as events such as the celebration of various countries’ independence days, multicultural nights and other cultural celebrations help instill a global outlook in APU students, encouraging understanding and respect for other nationalities and moulding them as effective communicators in tomorrow’s global economy.

IR4.0 adoption and development also calls for deeper critical-thinking skills. So to complement its world-class facilities, APU developed innovative teaching and learning methods that produce graduates who can think critically, act innovatively and communicate ideas effectively.

Ready to take on IR4.0

AS we cross the threshold of a brand new year, we are hurtling fast towards a future that increasingly resembles those depicted in sci-fi movies, with worlds that are increasingly enriched with integrated advanced technology in everyday lives.

Already many of these imagined technologies have already made headway within this decade – cars today are equipped with sensors that assist in driving and we have access to vast amounts of data, right on hand-held devices.

An accumulation of an ongoing tide of industrial revolutions going back to the invention of steam machinery in 18th century Britain, today’s Industrial Revolution 4.0 (IR4.0) is “blurring the distinction among physical, digital and biological spaces, ” as mentioned in the Ministry of International Trade and Industry’s (MITI) national policy called “Industry4WRD”.

The new revolution is set to change how products will be designed, made, used and operated. Maintaining and servicing these new products will also evolve, affecting how operations, processes, supply chain management and energy footprints in factories are utilised.


Nine Strong Pillars

Most industries worldwide look to the Boston Consulting Group’s (BCG) delineation of nine areas that will be affected by IR4.0. These include the processing of big data, the further advancement of artificial intelligence (AI) and robotics, increasing use of simulations.

Aided by use of cloud technology, technology will be more horizontally and vertically integrated, enabling Internet of Things (IoT), additive manufacturing, integration of augmented reality and cyber security to police against abuse.

While sweeping changes transform industries worldwide, IR4.0 should ultimately be instilled in those who will be affected – the country’s future crop of graduates coming out of the various academic institutions.

Ready for the future

At the forefront of this new wave, is one of Malaysia’s premier private universities, Asia Pacific University of Technology & Innovation (APU). It concentrates on providing a unique fusion of technology, innovation and creativity in preparing graduates for significant roles in business and society globally.

APU innovates by reviewing, developing and delivering programmes that are versatile, current and future-proof. By aligning its framework according to the Nine Pillars, it prepares its students for the impending revolution, through programmes that pivot on technology.

Grandmaster Warren say

Don't Drive car by looking in rear view mirror"

What he meant is this!

Don't look at past quarter of ARBB

LOOK AHEAD OF THE BILLIONS IN PROFITS AHEAD BY YEAR 2020 !

GOOD MORNING TO ALL GREAT INVESTORS OF ARBB 7181

CONGRATS TO ALL ARBB SHARE HOLDERS!
Hold tight when this rocket turn hot stock and fly VERY VERY SOON!

Secret number 1

ARBB is rebuilding the business to diversify from Timber to Technology services. It was a difficult decision to start up a new business line of developing the customised Enterprise Resource Planning (ERP) System, IR4.0 INDUSTRY WORLD IS THE FUTURE FOR EVERY COUNTRY!

Done deal already sorchai outsider still cannot see

Only THE REAL DEAL Eagle eye saw everthing

Just go COLLECT ARBB(7181) kaw kaw TODAY! BEFORE PRICES SHOOTS UP!

A good REMINDER for those who have not buy in for ARBB shares. This coming MONDAY please clean all sapu all ARBB(7181) shares when you still had the chance! SOMETHING VERY PROSPEROUS IS GOING TO HAPPEN TO ARBB(7181) SHARES!!!!!! GRAB IT GRAB ALL WHEN YOU STILL HAD THE GOOD OPPORTUNITY! HAVE A BLESSFUL WEEKEND GUYS!

Stick with ARBB (7181)

HOPEFULLY Next Target to cross is Rm0.70

After that Rm1.00!

The fundamental for ARBB (7181) can only get better as it has operations in Malaysia, China and coming soon neighboring country!!!!!

SO BE SMART, STAY WEALTHY. GO ALL IN SAPU CLEAN CLEAN ARBB (7181) SHARES BEFORE ITS TOO LATE, LOOKING AT THE TREND RIGHT NOW, THIS IS THE BEST CHOICE OF BULL STOCK IS IN THE MAKING, VERY VERY SOON!!!!!!

ARBB INDEED IS A Very smart AND A VERY PROSPEROUS company with profit margin of 33%!
SALUTE DATO LARRY LIEW

thank you Dato Larry Liew and his crew member! wishing you and ARBB7181 身体健康，万事如意

sapu sapu sapu, its time to sapu. those who buy under 0.30 congrats to you! me myself earn kaw kaw!

ARB posts RM12.8mil net profit in Q4

KUALA LUMPUR: Information technology firm ARB Bhd, formerly known as ATURMAJU RESOURCES BHD posted a net profit of RM12.8mil in the last quarter ended Dec 31, 2019.

This boosted its full year earnings to RM34.77mil, or 12 sen a share.

The company, in a filing with Bursa Malaysia today, said its improved results in the last quarter was driven by its IT business.

The group's timber business has ceased operations during the quarter under review.

"For IT segment, the wholly-owned subsidiary ARB Development Sdn Bhd. has entered a joint venture agreement in fourth quarter of 2019 which is expected to contribute positive future earnings for the group," it said.

ARB is principally involved in reselling of customised enterprise resource planning (ERP) software system, and Internet of Things, internet and multimedia development, as well as consultancy services.

To understand Industry 4.0, it is essential to see the full value chain which includes suppliers and the origins of the materials and components needed for various forms of smart manufacturing, the end-to-end digital supply chain and the final destination of all manufacturing/production, regardless of the number of intermediary steps and players: the end customer.

Enabling more direct models of personalized production, servicing, as well as customer/consumer interaction (including gaining real-time data from actual product usage) and cutting the inefficiencies, irrelevance and costs of intermediaries in a digital supply chain model, where possible, are some goals of Industry 4.0 in this customer-centric sense of increasingly demanding customers who value speed, (cost) efficiencies and value-added innovative services.

In the end, it remains business – with the innovative twist of innovation and transformation of business models and processes: increase profit, decrease costs, enhance customer experience, optimize customer lifetime value and where possible customer loyalty, sell more, and innovate to grow and remain relevant.

Industry 4.0: the fourth industrial revolution – guide to Industrie 4.0

Industry 4.0 is the digital transformation of manufacturing/production and related industries and value creation processes. Industrie 4.0 has been defined as the current trend of automation and data exchange in manufacturing technologies, including cyber-physical systems, the Internet of Things (IoT), cloud computing and artificial intelligence in creating the smart factory.

Industry 4.0 is a vision that evolved from an initiative to make the German manufacturing industry more competitive (‘Industrie 4.0’) to a globally adopted term referring to industrial transformation in discrete and process manufacturing.

Industry 4.0 is the evolution to cyber-physical systems, representing the fourth industrial revolution on the road to an end-to-end value chain with Industrial IoT and decentralized intelligence in manufacturing
Industry 4.0 is often used interchangeably with the notion of the fourth industrial revolution.

It is characterized by, among others, 1) even more automation than in the third industrial revolution, 2) the bridging of the physical and digital world through cyber-physical systems, enabled by Industrial IoT, 3) a shift from a central industrial control system to one where smart products define the production steps, 4) closed-loop data models and control systems and 4) personalization/customization of products. The goal is to enable autonomous decision-making processes, monitor assets and processes in real-time, and enable equally real-time connected value creation networks through early involvement of stakeholders, and vertical and horizontal integration.

Industry 4.0 is a vision and concept in motion, with reference architectures, standardization and even definitions in flux. Most Industry 4.0 initiatives are early-stage projects with a limited scope. The majority of digitization and digitalization efforts, in reality, happen in the context of third and even second industrial revolution technologies/goals.

In essence, the technologies making Industry 4.0 possible leverage existing data and ample additional data sources, including data from connected assets to gain efficiencies on multiple levels, transform existing manufacturing processes, create end-to-end information streams across the value chain and realize new services and business models.

VERY VERY HAPPY WITH ARB q4 EARNINGS! ITS TIME TO SAPU ALL ARB SHARES!

GOOD MORNING TO ALL ARBB 7181 FIGHTERS!

HAHA YOU GUYS STILL HERE? HANDS GOT BURN?

GOOD MORNING TO ALL ARB7181 FIGHTERS

Good Articles to Share
Timeline: The long road to Trump's impeachment and trial

WASHINGTON - The U.S. Senate was poised on Wednesday to acquit President Donald Trump on charges of abuse of power and obstruction of Congress.

Here is a look at the road to his impeachment and trial.

MUELLER REPORT

In July 2017, two little-known Democrats in the House of Representatives make the first attempt to impeach Trump, basing it on investigations into Moscow's interference in the 2016 U.S. elections.

They file formal charges known as articles of impeachment, alleging that Trump obstructed justice by firing FBI head James Comey to hinder the Russia investigation by then Special Counsel Robert Mueller.

After a two-year probe, Mueller finds insufficient evidence of a criminal conspiracy involving Trump and draws no conclusions on whether Trump obstructed justice.

Little comes of this impeachment effort. In 2019 House Speaker Nancy Pelosi, the leading Democrat in Congress, says Trump is "just not worth it."

WHISTLEBLOWER

On Aug. 12, 2019, an anonymous intelligence official files a whistleblower complaint about a phone call between Trump and Ukrainian President Volodymyr Zelenskiy. According to a summary of the call later released by the White House, Trump asked Zelenskiy to investigate both former U.S. Vice President Joe Biden, a Democrat running for president in 2020, and a conspiracy theory that Ukraine, not Russia, was behind 2016 election meddling.

IMPEACHMENT INVESTIGATION

Amid further reports of Trump's dealings with Ukraine, Pelosi on Sept. 24 accuses Trump of urging a foreign power to intervene in the 2020 election and announces an impeachment investigation in the Democratic-controlled House.

In testimony at televised hearings, U.S. diplomats and officials describe a pressure campaign organised by Trump's lawyer, Rudy Giuliani, to get Ukraine to announce the Biden investigation. Trump's freezing of $391 million in aid to Ukraine was part of the campaign, according to some testimony.

TRUMP'S DEFENCE

Trump says he was just trying to get Kiev to quash corruption and points to the fact that the Ukrainian investigations never happened as evidence that the July call was innocent. Republican lawmakers say that even if Trump's conduct was not perfect, it does not belong in the category of "high crimes and misdemeanours" stipulated in the U.S. Constitution as a reason to impeach a president.

IMPEACHED

On Dec. 18 Trump becomes only the third U.S. president to be impeached when the Democratic-led House approves two articles of impeachment charging him with abuse of power and obstructing Congress.

THE TRIAL

The Senate puts Trump on trial in late January. The outcome seems in little doubt, as Republicans dominate the chamber where a two-thirds majority is needed to convict and remove the president. No Republican expresses a desire to find Trump guilty.

WITNESSES

The two parties tussle over Democrats' attempts to introduce new witnesses and documents to the trial. They are especially keen to hear from former national security adviser John Bolton who had disparaged efforts by Trump allies to influence Ukraine as "a drug deal."

THE BOLTON FACTOR

The New York Times reports that Bolton, a foreign policy hawk who has served previous Republican administrations, has written a book in which he says Trump told him he had wanted to continue freezing the aid to Ukraine to pressure Zelenskiy to help with investigations into Democrats, including Biden. The revelation contradicts Trump's defence. It puts pressure on a handful of moderate Republican senators to join Democrats and vote to call Bolton and White House officials as witnesses. But a majority of senators vote to reject new witnesses.

TRUMP DEFENCE

Trump lawyer Alan Dershowitz says at the trial that senators cannot impeach a president for doing anything to try to win re-election if the president believes that to be in the public interest.

Democrats and constitutional experts severely criticize this defence, saying it gives U.S. presidents huge new powers not foreseen in the Constitution. Dershowitz says later his remarks were deliberately misinterpreted.

IoT security and privacy issues
The internet of things connects billions of devices to the internet and involves the use of billions of data points, all of which need to be secured. Due to its expanded attack surface, IoT security and IoT privacy are cited as major concerns.

In 2016, one of the most notorious recent IoT attacks was Mirai, a botnet that infiltrated domain name server provider Dyn and took down many websites for an extended period of time in one of the biggest distributed denial-of-service (DDoS) attacks ever seen. Attackers gained access to the network by exploiting poorly secured IoT devices.

Because IoT devices are closely connected, all a hacker has to do is exploit one vulnerability to manipulate all the data, rendering it unusable. Manufacturers that don't update their devices regularly -- or at all -- leave them vulnerable to cybercriminals.

Additionally, connected devices often ask users to input their personal information, including names, ages, addresses, phone numbers and even social media accounts -- information that's invaluable to hackers.

However, hackers aren't the only threat to the internet of things; privacy is another major concern for IoT users. For instance, companies that make and distribute consumer IoT devices could use those devices to obtain and sell users' personal data.

Beyond leaking personal data, IoT poses a risk to critical infrastructure, including electricity, transportation and financial services.

The future of IoT
There is no shortage of IoT market estimations. For example, a few include:

Bain & Company expects annual IoT revenue of hardware and software to exceed $450 billion by 2020.
McKinsey & Company estimates IoT will have an $11.1 trillion impact by 2025.
IHS Markit believes the number of connected IoT devices will increase 12% annually to reach 125 billion in 2030.
Gartner assesses that 20.8 billion connected things will be in use by 2020, with total spend on IoT devices and services to reach $3.7 trillion in 2018.

AWS IoT, a cloud platform for IoT released by Amazon. This framework is designed to enable smart devices to easily connect and securely interact with the AWS cloud and other connected devices.
ARM Mbed IoT, a platform to develop apps for the IoT based on ARM microcontrollers. The goal of the ARM Mbed IoT platform is to provide a scalable, connected and secure environment for IoT devices by integrating Mbed tools and services.
Microsoft’s Azure IoT Suite, a platform that consists of a set of services that enables users to interact with and receive data from their IoT devices as well as perform various operations over data, such as multidimensional analysis, transformation and aggregation, and visualize those operations in a way that’s suitable for business.
Google’s Brillo/Weave, a platform for the rapid implementation of IoT applications. The platform consists of two main backbones: Brillo, an android-based operating system for the development of embedded low power devices; and Weave, IoT-oriented communication protocol that serves as the communication language between the device and the cloud.
Calvin, an open source IoT platform released by Ericsson designed for building and managing distributed applications that enable devices talk to each other. Calvin includes a development framework for application developers as well as a runtime environment for handling the running application.
Consumer and enterprise IoT applications
There are numerous real-world applications of the internet of things, ranging from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). IoT applications span numerous verticals, including automotive, telecom and energy.

In the consumer segment, for example, smart homes that are equipped with smart thermostats, smart appliances and connected heating, lighting and electronic devices can be controlled remotely via computers and smartphones.

Wearable devices with sensors and software can collect and analyze user data, sending messages to other technologies about the users with the aim of making users' lives easier and more comfortable. Wearable devices are also used for public safety -- for example, improving first responders' response times during emergencies by providing optimized routes to a location or by tracking construction workers' or firefighters' vital signs at life-threatening sites.

In healthcare, IoT offers many benefits, including the ability to monitor patients more closely to use the data that's generated and analyze it. Hospitals often use IoT systems to complete tasks such as inventory management, for both pharmaceuticals and medical instruments.

Smart buildings can, for instance, reduce energy costs using sensors that detect how many occupants are in a room. The temperature can adjust automatically -- for example, turning the air conditioner on if sensors detect a conference room is full or turning the heat down if everyone in the office has gone home.

In agriculture, IoT-based smart farming systems can help monitor, for instance, light, temperature, humidity and soil moisture of crop fields using connected sensors. IoT is also instrumental in automating irrigation systems.

In a smart city, IoT sensors and deployments, such as smart streetlights and smart meters, can help alleviate traffic, conserve energy, monitor and address environmental concerns and improve sanitation.

IoT touches every industry, including healthcare, finance, retail and manufacturing. Smart cities help citizens reduce waste and energy consumption and connected sensors are even used in farming to help monitor crop and cattle yields and predict growth patterns.

As such, IoT is one of the most important technologies of everyday life and it will continue to pick up steam as more businesses realize the potential of connected devices to keep them competitive.

Benefits of IoT
The internet of things offers a number of benefits to organizations, enabling them to:

Monitor their overall business processes;
Improve the customer experience;
Save time and money;
Enhance employee productivity;
Integrate and adapt business models;
Make better business decisions; and
Generate more revenue.
IoT encourages companies to rethink the ways they approach their businesses, industries and markets and gives them the tools to improve their business strategies.

Pros and cons of IoT
Some of the advantages of IoT include:

Ability to access information from anywhere at any time on any device;
Improved communication between connected electronic devices;
Transferring data packets over a connected network saves time and money;
Automating tasks helps improve the quality of a business’ services and reduces the need for human intervention.
Some disadvantages of IoT include:

As the number of connected devices increases and more information is shared between devices, the potential that a hacker could steal confidential information also increases;
Enterprises may eventually have to deal with massive numbers -- maybe even millions -- of IoT devices and collecting and managing the data from all those devices will be challenging.
If there’s a bug in the system, it’s likely that every connected device will become corrupted;
Since there’s no international standard of compatibility for IoT, it’s difficult for devices from different manufacturers to communicate with each other.
IoT standards and frameworks
There are several emerging IoT standards, including:

6LoWPAN (IPv6 over Low -Power Wireless Personal Area Networks), an open standard defined by the Internet Engineering Task Force (IETF). The 6LoWPAN standard enables any low-power radio to communicate to the internet, including 804.15.4, Bluetooth Low Energy and Z-Wave (for home automation).
ZigBee0, a low-power, low data-rate wireless network used mainly in industrial settings. ZigBee is based on based the IEEE 802.15.4 standard. The ZigBee Alliance created Dotdot, the universal language for IoT that enables smart objects to work securely on any network and understand each other.
LiteOS, a Unix-like operating system for wireless sensor networks. LiteOS supports smartphones, wearables, intelligent manufacturing applications, smart homes and Internet of Vehicles (IoV). The operating system also serves as a smart device development platform.
OneM2M, a machine-to-machine service layer that can be embedded in software and hardware to connect devices. The global standardization body, OneM2M, was created to develop reusable standards to enable IoT applications across different verticals to communicate.
DDS (Data Distribution Service) was developed by the Object Management Group (OMG) and is an IoT standard for real-time, scalable and high-performance machine-to-machine communication.
AMQP (Advanced Message Queuing Protocol), an open source published standard for asynchronous messaging by wire. AMQP enables encrypted and interoperable messaging between organizations and applications. The protocol is used in client/server messaging and in IoT device management.
CoAP (Constrained Application Protocol), a protocol designed by the IETF that specifies how low-power compute-constrained devices can operate in the internet of things.
LoRaWAN (Long Range Wide Area Network), a protocol for wide area networks, it’s designed to support huge networks, such as smart cities, with millions of low-power devices.
IoT frameworks include: