KLSE: ARBB (7181)       ATURMAJU RESOURCES BHD MAIN : Industrial Products
Last Price Today's Change   Day's Range   Trading Volume
0.275   0.00 (0.00%)  0.265 - 0.28  6,155,000
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Ann. Date Date Type Designation Name View
31-Jan-2019 31-Jan-2019 APPOINTMENT Member of Remuneration Committee KHOR CHIN MENG View Detail
31-Jan-2019 31-Jan-2019 RESIGNATION Member of Remuneration Committee HO PUI HOLD View Detail
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THEREALDEAL wow bettertomolo you work inside arb? i dont think so
17/02/2020 12:49 PM

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.
17/02/2020 12:51 PM
THEREALDEAL 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.
17/02/2020 12:52 PM
Elaine Tan Of so many counters this ARBB7181 has a lot of naysayers.

What are the lessons?

No. 1

It has trained many of my friends to be strong defenders and debaters
It sharpened our skill and reconfirm what we hold everyday

No. 2

Most naysayers are prejudiced with preconceived notion

They think like the 80% majority who cannot see prospects or coming value.
Only 20% can see value in ARBB7181

No. 3

Naysayers who missed ARBB7181 bull run never stop to think. Why they missed? And because they did not learn so they will repeat the same mistake again and again

No. 4

Looks like naysayers are a bunch of nuisance to all of us who believe?

What good are they?

Yes. i thinks there is one good reason.
Last time one person kept a number of great fishes in a tank. But the fishes were well fed and lethargic. They just lie at the bottom of the tank.

So he put SHIT EATING CAT FISH in the tank
The cat fish were bottom SHIT feeders. So the other fishes generally avoided those nuisance SHIT EATING CAT FISH AND SWIM TO higher SIDE OF THE FISH TANK.

Because of this they were difference between vigilant and healthy fishes

Ya. All are kept alert by nuisance posts, when the price is fair, we just keep on collecting it

So keep strong

All comrades in ARBB7181

Be strong. Everyday is a day nearer to ARBB7181 job award
17/02/2020 12:57 PM
Elaine Tan Here's WHY I'm still holding ARBB 7181

-ARBB 7181 has the lowest PE compares to other INDUSTRIAL stock, we believe stock price is now undervalued, will catch up Greatech, UWC, Kesm growth, soon or sooner

-Benefits from Industry 4.0

-Benefits from budget 2020

-Coming soon generous dividend payout
17/02/2020 12:59 PM

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.
18/02/2020 8:58 AM
THEREALDEAL 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.
18/02/2020 8:58 AM
Elaine Tan thanks therealdeal, happy reading guys. Arb7181 is the best choice, next wave is coming in, buy all arb shares now!
18/02/2020 9:23 AM
huntertee Tp 1.00 hahahahahahh
18/02/2020 3:26 PM
RJ87 only converted 5mil trade receivables into cash last quarter.

But raise bunch of cash from ICPS and ICPS conversion and invest in property plant and equipment and acquiring subsidiary.
18/02/2020 10:05 PM
kenny chua good morning happy people of arb
19/02/2020 9:16 AM
Elaine Tan good morning kenny chua
19/02/2020 9:17 AM

Yes in this months buy call on Arb7181 at 0.23 sen proven STRIKE ON!

Now ALREADY UP TO at LEAST 0.29 sen


going forward as BULL RAGING RUN AGAIN!

So remember to get on the BOARD fast

Once ARB7181 secures job awards

Zooooooom and all who missed will by pulling your hair and grinding your teeth

But those who bought? TP RM1.00

19/02/2020 9:17 AM
THEREALDEAL wow morning to both of you and to all arbb7181 fighters!
looking good today
19/02/2020 9:33 AM

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.

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.
19/02/2020 9:35 AM
THEREALDEAL 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.
19/02/2020 9:35 AM
RJ87 Eh you two, do u have any idea who are their customer and where they get their 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?

Any ARBB "fighter" can explain?
19/02/2020 9:15 PM
TalkNumberOne Dumbo101, I wish i can declare my revenue as my profit. This will be the best business in the world. Not promoting ARBB. But stupidity has a limit. I guess Dumbo won't be able to understand what I meant. Bottom line is don't act smart, makes you look like an idiot.
20/02/2020 7:35 AM
THEREALDEAL 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!
20/02/2020 9:39 AM
THEREALDEAL hahaha dumbo jumbo small kids crying all day long for father mother
20/02/2020 9:40 AM
THEREALDEAL 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!
20/02/2020 9:47 AM
靓女 phoon phoon FOR THOSE DUMB FLOWERCRAP KIDS- A good investor will only hold 5-6 shares in their profile. for MY case, unless I AM A MULTI-BILLIONAIRE, I WILL BE able to invest 30-50 counters, if not, my opinion is that I only invest peanuts in every single account
20/02/2020 9:56 AM
THEREALDEAL yes agree with 靓女 phoon phoon
20/02/2020 9:57 AM
THEREALDEAL 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
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.
20/02/2020 10:02 AM
kenny chua wow i am all ears guys
Tech ir4.0 industry
Digital Economy
Visit Malaysia Year.
20/02/2020 10:03 AM
RJ87 Go see the balance sheet yourself. Trade Receivables 10mil in Q4,18 and 49mil in Q4,19. I'm quite sure you can see that if you are NOT BLIND.

If ur Revenue is RM100mil, RM70mil is your cost + expenses. Your profit is 35mil, Trade receivables increased by 39mil. Technically, u r operating at negative 4mil in cash.

I follow this counter 2 quarters ago. The more I read, the more dodgy it appears to me.Bought in at 33sen, went up to 39sen, and exited everything at 37sen. Market cap 80mil, if it goes to ZERO, it will just go to ZERO. You don't know where are the customers. You don't know what asset they own. Out of the 1.4Bil ICPS, I don't know who owns what. AFAIK, it could be Datuk Yeo himself. Maybe should run background check on him how much he owns ARBB shares, and if any of his associates are his customer. Private Companies 9.7%,28,181,961 shares; Individual Insiders 16.2%, 46,916,612 shares; General Public 74.1%, 214,714,127 shares

You can invoice as much as you like every year. But when you decide to write off in 2020; your 2019 35mil profit will just vaporize. Maybe you are happy to keep the numbers in form of invoices. MoU instead of LOA. I prefer profit appears in operating cash. Let just say this is a stunt they are pulling...then, do show also do in full. Collect payment, increase operating cash. I will myself value ARBB at PE6 instead of PE2. Now, everything in invoices...you know la...you want say what later also can. Until these "profits" appears as cash. I join at PE6 or 10 nvm. If doesn't appear in cash, give me PE0 .5also I don't want. That's the reason i'm still here. I wanna know when they are gonna turn profit in cash and actually have a real business.

Hwa Siong Chong, Phooi San Khoo, Chee Kei Lee, You King Wong, Yan Shuen Er, Boon Siong Lee, Yee Au, Kok Liew. Your name aren't listed; hahahha...you not that big a deal anyway. =P
And if you are, u better come punt for ARBB every second. And I hope you are close to this Datuk Yeo and Kok Liew. At least, if they are screwing the 74.1% general public over. You get heads up. Market cap 80mil, ICPS exercise and issuance raised 52mil. Cash balances left 25.6mil. Invested 20.9mil in what not disclosed. Anyone here bother to find out? Or u just interested in punting IR4.0 only.

Yeah, I'm pretty dumb when comes to ARBB. I'm quite sure it's better to be dumb and find out what I don't know about ARBB than be WILLFULLY BLIND and DUMB. If you are dumb, you can learn. If you willfully BLIND, YOU STAY DUMB.
20/02/2020 7:03 PM
kenny chua hahahaha creep of the day rj87, cry more baby cry
20/02/2020 8:32 PM
alipay88 wow kennychua how come rj87 cry so much? is he losing his money? burn his hand or drop his pants in investing? hehe
20/02/2020 8:36 PM
kenny chua alipay88, i think he is more than that, maybe his wife also follow others lari jalan, hahahaha
20/02/2020 8:37 PM
kenny chua THEREALDEAL 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!
20/02/2020 9:47 AM see this morning what therealdeal write, people are earning money here, but this few baffons are burning their balls! hahaha
20/02/2020 8:38 PM
kenny chua you want more ir4.0 clowns? nah
20/02/2020 8:39 PM
kenny chua BEHOLD ARBB 7181 IR4.0: What Can SMEs Expect?

The role of technology as a catalyst of continuous growth and transformation is irrefutable. From the first to the third industrial revolutions, technology has been at the centre of some monumental machine-driven inventions that led to extensive improvements across the globe. Technology is once again at its peak, as local industries find themselves at the brink of another imminent revolution – the Fourth Industrial Revolution, or more commonly known as IR4.0.

Recently, Prime Minister Tun Dr Mahathir launched the “Industry Forward” (Industry4WRD) National Policy to position Malaysia as a primary hub for smart manufacturing and high-tech industries. By 2025, Industry4WRD is expected to raise the economic contribution of the manufacturing sector by 54% from US61bil to US$94bil.

In Malaysia, SMEs are urged to seize the myriad of opportunities bound to arise from this newest revolution. Some of the main examples of technology arising from IR4.0 include self-driving vehicles, delivery drones, and robotics for higher level of automation in manufacturing. While IR2.0 was focused on mass production and IR3.0 emphasised automation, IR4.0 is expected to modernise Malaysia’s industrial capability and transform our economy into a high-value manufacturer.

*Source: ResearchGate
A Boost from the Government & Corporates
Benefits of moving on to Industry 4.0 are aplenty; SMEs get to increase their productivity, flexibility, and efficiency, more rigorous R&D activities can be done, better quality of products can be made, and the potential of developing new homegrown talent are incredibly immense.

In order to assist SMEs to achieve this, the government has made available the Industry 4.0 Readiness Assessment (Industry4WRD-RA), a free online assessment to determine the type of assistance that organisations may need, including an allocation of RM210 million to facilitate this process. To qualify for these matching grants, the SMEs must adopt management- and operation-level recommendations from the exercise, such as improving the quality and skills of their workforce, promoting strategic collaborations to improve creation of smart technologies and business growth, and enhancing last mile connectivity in high impact areas.

Apart from the government, technology enablers like telco giant Celcom has introduced business solutions such as Celcom Cloud Secure which protects data in your emails and websites, and Machine-to-Machine (M2M) Connected Services for real-time operational efficacy through IoT technology. These accessible solutions lower the barriers of entry for SMEs to run their businesses the IR4.0 way.
20/02/2020 8:41 PM
kenny chua 5G leads the way
A main vehicle in IR4.0 is 5G technology, which will significantly increase efficiency through a higher level of automation, cutting-edge robotics, data driven insight, and unprecedented global connectivity. To equip Malaysian SMEs to ride the 5G wave, the government has also budgeted a 5G ecosystem development grant worth RM50 million.

Recognising the importance of 5G empowerment, Celcom and fellow telecommunication heavyweight Maxis have come together for a collaboration that will enable both companies to explore possible business opportunities in key strategic areas to deliver the best 5G innovations that will benefit the people and businesses in Malaysia.

The possibilities are endless for SMEs with 5G – the high-speed connectivity will be able to meet the speed and connectivity that Internet of Things (IoT) demands, unlocking a treasure trove of opportunities for businesses. From IT, manufacturing, retail, transportation and education to health care, agriculture, and entertainment, almost every industry will be able to benefit from 5G connectivity to traverse the Industrial Revolution 4.0.

Powered by 5G, businesses will have a bigger sandbox to experiment in while leveraging on existing disruptive technologies such as drones, robotics, and artificial intelligence. Businesses can also expect to become more agile – personnel employed remotely will be able to communicate and collaborate with other colleagues more effectively, including seamless access to shared cloud data across enterprises.

The future is here
Nevertheless, in addition to 5G adoption and the Industry 4WRD National Policy grants, various other opportunities are also available under programmes such as the National IoT Strategic Roadmap and National Fiberisation and Connectivity Plan (NFCP) that SMEs should embrace to be ready for the demands of IR4.0. As of this year, about 20% of companies in the country have already migrated to IR4.0 while the rest of multinational companies and SMEs being expected to follow suit swiftly in successive years.
20/02/2020 8:41 PM
20/02/2020 8:42 PM
traderstrades good night guys
20/02/2020 9:25 PM
gooddaymate hi matey ,so late still here chit chatting, good night
20/02/2020 9:27 PM
THEREALDEAL good night to all arbb fighters!
20/02/2020 9:31 PM
THEREALDEAL 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.
21/02/2020 10:15 AM
RJ87 The ICPS can be converted into new Shares at the conversion price of RM0.20 ("Conversion Price") in the following manner:

(a) by surrendering of such number of ICPS with an aggregate value equal to the Conversion Price to be converted for 1 new Share; or

(b) a combination of such number of ICPS and cash with an aggregate value equal to the Conversion Price, subject to a minimum of 1 ICPS, and paying the difference between the aggregate value of the ICPS surrendered and the Conversion Price in cash for 1 new Share.

Total issue ICPS 1000mil shares.
Proceed from corporate exercise of ICPS 10mil
Proceed from share capital via conversion of ICPS 42mil/0.2 = 210mil shares.

opportunity lies at those held mothershare before ICPS. ICPS at 0.01sen. Add 0.2 to convert. Sell open market 0.275 to 0.4 by setting value trap. Making 30% to 90%. This exercise only benefits the company and ICPS owner. You buy in now, you are gonna deal with 800mil shares dilution.

Knowing this fact, its not all bad. At least, there are still 800-1200mil ICPS holding on their share. Either don't hv the 20sen capital to convert or believe ARBB can do better.

Current Market Cap of 80mil, Current asset is worth 77mil.
Full conversion of 800 outstanding ICPS will add another 160mil to 240mil will cash into ARBB.
making total Current Asset worth 240mil to 320mil. So much cash...no plans...now, I don't know where the 45mil investment went to. No words about it. Don't be surprise if ARBB becomes like Sanichi. You know, raise money from equity...then become a developer...

If market value this business at Market Cap 400-450mil, then the price is 0.24-0.265. There is not much downside. Very attractive for a company with 100mil Revenue make 35mil profit IF 35mil profit are CASH not trade receivables.

See better make sound investment decision than to listen if Kenny Chua farts. If he farts, ARBB is good. I can't relate how he farts will make ARBB more profitable. I'm just not that type of investor. I look at numbers.

Upto you to choose which "investor" you are.
21/02/2020 10:16 AM
RJ87 Sanichi Technology Berhad, an investment holding company, is engaged in the design and fabrication of precision moulds and tooling for use in automobile, home appliance, audio visual, computer peripheral, electrical and telecommunication industry in Malaysia. It also offers plastic injection and conventional plastic injection moulds. The company was founded in 2000 and is based in Johor Bahru, Malaysia.

suddenly, got a subsidiary called Sanichi Property. Developer for Marina Point. Ahahahaha...
Investor kena pu nicely there. haahahhaha
21/02/2020 10:18 AM
Elaine Tan I now has 15% invested in shares

Already up 200% from 5%

So why are you guys so concerned ?

Most of it is profit


Follow ME

MY calls all to buy winning shares

"Whatsoever he doeth shall prosper"

" Joseph was a prosperous man because the LORD is with him"
21/02/2020 10:44 AM

Big data

Posted by: Margaret Rouse

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.
21/02/2020 10:45 AM
THEREALDEAL 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
21/02/2020 10:46 AM
THEREALDEAL 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.
21/02/2020 10:46 AM
THEREALDEAL 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.
21/02/2020 10:47 AM
THEREALDEAL 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."
21/02/2020 10:48 AM
RJ87 Show how did u come to this number 200% from 5%? I think it has something to do with ICPS. Now, people without ICPS, how to do what u did?

Elaine Tan I now has 15% invested in shares

Already up 200% from 5%

So why are you guys so concerned ?

Most of it is profit


Follow ME

MY calls all to buy winning shares

"Whatsoever he doeth shall prosper"

" Joseph was a prosperous man because the LORD is with him"
21/02/2020 11:33 AM
RJ87 If buy ICON at 8sen, sold at 80sen like urusharta did, u made whopping 1000%.

The problem is if u buy at 80sen and now it's 12sen, u think should be concerned or not?
21/02/2020 11:35 AM
RJ87 THEREALDEAL, Amazon Q4,19 earnings beats estimates. Jeff Bezos is USD15Bil richer.
Is ARBB make going to make RM1Bil in revenue?
21/02/2020 11:44 AM
Jeffreyteck Big profit without dividends and cash is as good as 0 profit.
21/02/2020 11:08 PM


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