DBMS / BusinessIntelligence, BusinessIntelligence / BigData
Orsys, with 30 years of experience, is providing high quality, independant State of the Art seminars and hands-on courses corresponding to the needs of IT professionals. Orsys proposes a set of courses on the most important topics in IT technologies and management.
• refers to applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations.
– Businessintelligence systems can help companies gain more comprehensive knowledge of the factors affecting their business, and help companies to make better business decisions.
Use of a fully integrated HRMS – like DLGL (or others) – would be a huge step in moving towards bigdata capability because ALL worker-related data will be standardized and available. In fact, when compared to a mix of interfaced systems a well-integrated HRMS is bigdata – about people.
That is the challenge of system implementation – to work through all of the data and process complexities. Organizations that fail to do this, or worse, fail to maintain it, will discover that their HR data is not accurate, complete, or timely. Of course this frequent condition of human resource data is no secret to payroll. That is why so many payroll managers want their own data system separate from any HR database (and why one organization’s payroll and HR systems duplicate most data; duplicate, but not identical).
3.3 MapReduce-based Platforms
The MapReduce model, although highly flexible, has been found to be too low-level for routine use by practitioners such as data analysts, statisticians, and scientists (Olston et al., 2008; Thusoo et al., 2009). As a result, the MapReduce framework has evolved into a MapReduce ecosystem shown at Figure 2, which includes a number of (i) high-level interfaces added over the core MapReduce engine, (ii) application development tools, (iii) workflow management systems, and (iv) data collection tools. High-level Interfaces: The two most prominent examples of higher-level layers are Apache Hive (Thusoo et al., 2009) with an SQL-like declarative interface (called HiveQL) and Apache Pig (Olston et al., 2008) with an interface that mixes declarative and procedural elements (called Pig Latin). Both Hive and Pig will compile the respective HiveQL and Pig Latin queries into logical plans, which consist of a tree of logical operators. The logical operators are then converted into physical operators, which in turn are packed into map and reduce tasks for execution. The execution plan generated for a HiveQL or Pig Latin query is usually a workflow (i.e., a directed acyclic graph) of MapReduce jobs. Workflows may be ad-hoc, time-driven (e.g., run every hour), or data-driven. Yahoo! uses data-driven workflows to generate a reconfigured preference model and an updated home-page for any user within seven minutes of a home-page click by the user.
About the Program. The Master of Science in Business Analytics is 33 credits in length and provides a strong quantitative foundation that is inclusive of advanced statistics, data mining, text mining, tools for analysis and presentation and other relevant courses. The mission of the program is to develop in working professionals the skill sets needed to address the massive amount of data that has become universally available in order to leverage this toward successful business and decision-making applications. Numerous business functions and industries have noted the enormous need for individuals who possess the quantitative, analytical and presentation skills required to apply data to the solution of business problems, to create new business opportunities and to support innovative practices. These skills are also critical to decision-making in the nonprofit, governmental and educational industries as well as to entrepreneurship and small business management. All courses are offered online.
Today, the data which companies have turned them into a gold mine, so to speak, and they are like a treasure under a big rock which is waiting to be discovered. In order to reach this valuable treasure, like a master miner, one should make a deep research, dig the right places, make the right analyses and should provide right methods and techniques which will make this valuable treasure unique. Businessintelligence solutions with the BigData are currently in high demand thanks to the companies that aim at coping with these large data stream, in order to survive in this competitive market environment in future. Companies will gain strength in this competitive environment after the correct analyses of social media communications, income statements, the relation between sales and advertisement budgets, office documents, stock market data, investment rates, and even the liquid instant data which is streaming in websites. The data streaming from numerous sources will worth golds if it is processed. However, at the same time, it may bring big problems and security flaws alongside. In this regard, it is understood that companies should make significant investments in BigData solutions.
the analytics and improve their own business like Facebook or eBay; others like BuzzStream or Crowdbooster
Another upcoming big challenge for the companies is that existing data processing systems cannot handle the exponentially growing load. Petabytes of data, incredible pace in which this data is coming, huge variety of sources, machine-generated, social and unstructured data - era of BigData is not the future, it’s our present.
Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, retail analytics, store assortment and stock- keeping unit optimization, marketing optimization and marketing mix modelling, web analytics, sales force sizing and optimization, price and promotion modelling, predictive science, credit risk analysis, and fraud analytics. Since Abstract: A forecast from International Data Corporation (IDC) sees the bigdata technology and services market growing at a compound annual growth rate (CAGR) of 23.1 per cent over the 2014-2019 forecast period with annual spending reaching USD 48.6 billion in 2019. This wide range includes domain such as Banking, Government, Manufacturing, Utilities, Telecommunications Education, Healthcare, Retail, Insurance, Securities, Railroads, Customer Service / BPO etc.
The objective of our work has been to address the solution space of real-time analytics and data
collection on the fly by using a new approach. Important requirements have been to be able to insert the solution with minimal integration. It is also a well known fact that in real-time systems you should avoid network delay and round-trips in order to improve response times. We have therefore explored how to co-locate collection and analytics at the same node, while maximizing performance in each single node. With this approach, in some cases it is possible to handle an unprecedented number of operations for collection and analytics in a single node. We prove in our solution that it is possible to reach such performance that analyzing all data from a super size business scenario can be done in real- time by using one single business analytics router. Although large scale analytics can be provided in a properly located single node in our solution, we are not at all against distribution. On the contrary, distribution should as always be used as a scalability option, for redundancy/availability, or because retrieving the data in one point is simply not feasible. Our solution can therefore be scaled out by performing real-time analytics locally and then streaming the results on the fly from local nodes to nodes that perform meta analytics (a k a higher order analytics). An important part of our work has been to support our in-memory data engine with persistent real-time storage. Considering the typical data speed that can be handled by the in-memory engine, this is a quite challenging task.
Srikantaiah K C, Professor in the Department of Computer Science and Engineering at S J B Institute of Technology, Bangalore, India.
Venugopal K R, Vice-Chancellor of Bangalore, University.
hidden in mining, correlation of unknown, trends in market and preferences of customer etc., this information helps the organization to establish their decision for business. When high computing power systems are used to designed for specialized system of analytics. Bigdata analytics offers many benefits for business are new revenue opportunities, more effective marketing, better customer service, improved operational efficiency, Competitive advantages over rivals by increasing accuracy it is easy to make decision confidently and that will result in efficiency and reduces the cost and risk.
A.L.I.C.E (Artificial Linguistic Internet Computer Entity) is a natural language processing chatbot , developed by Dr.
Richard Wallace. It has its own markup language called AIML (Artificial Intelligence Markup Language), and earned the Loebner Prize in 2000 and 2001. ALICE applies heuristic pattern matching algorithm to inputs to obtain suitable matching pattern in AIML and this algorithm uses a depth first search technique with backtracking. The knowledge base of this system is composed of AIML files, which is an extension of the widely used XML format. This XML compliant dialect for encoding the behavior of a chatbot in a standardized form can be exchanged between different chatbot interpreters and implementations. AIML is highly recursive and typically a single input-response pattern will have many alternative patterns matches. An AIML dataset typically consists of pattern and template that can be matched to the user's input statement and the corresponding response.
• Data integration tools for the usage patterns involving physical data movement
• Connectivity options, including corporate network, public network, or open VPN In some cases an existing investment in a tool that is part of Intel’s plan of record (PoR) may supersede the decision to use other available tools. For example, if an adequate dataanalysis tool is already part of the PoR, then it may not be appropriate to invest in another product just because it is available in the cloud. We recommend that a cloud BI solution use new cloud- based tools only if the PoR tools do not meet business requirements or provide the necessary capabilities.
To alleviate the shortcoming of retrieving limited product reputation via survey data. An automatic framework to monitor the reputation of a variety of products by mining Web contents. Clustering and association mining techniques are among the most common methods employed to support reputation management applications. More recently a reputation management method which not only mines text- based reputation data from the Web but also considers the graphical images of products posted to the Web. Nevertheless, by the time of this writing, twenty billion images have been uploaded to Instagram. 1 Given such an extraordinary size of images archived online, it is extremely challenging to analyse the sheer volume of images for product reputation management, not to mention the variety of formats of source data (e.g., text versus images). To carry out an automatic analysis of the textual comments posted to the Web for product reputation management, it is essential to develop a rich computer-based representation of product information for subsequent product reputation analysis. Recently, an automated product ontology mining method that is underpinned by latent topic modeling has been explored to build product ontologies based on textual descriptions of products extracted from online social media . The automatically constructed product ontologies can be used as the basis to support product reputation management applications and other marketing intelligence applications. However, given the computational complexities involved in automated product ontology extraction from online social media, new computational methods must be developed to cope with the volume,
• 68% of data is created and consumed by consumers — watching digital TV, interacting with social media, sending camera phone images and videos between devices and around the Internet, and so on
• But enterprises have liability or responsibility for nearly 80% of the information in the digital universe
In today’s economy, corporations everywhere must squeeze inefficiency out of all their operational processes as quickly as they can to avoid higher costs and reduce wastefulness. The ability to detect these inefficiencies in real time is another good use case for RT operational intelligence. An example here is supply chain management. Often a company runs the risk of stock-outs due to a misalignment between customer demand for a product and the company’s inventory resulting in significant revenue loss as well as customer dissatisfaction. Being able to detect these problems early reduces missed shipments, overages and underages and reduces costly emergency replenishment due to process inefficiency. As another example, certain types of complex equipment and devices such as cell towers, networks, aircraft, oil wells and automobiles often provide “signals” of pending failures. Unfortunately many organizations are not able to recognize these signals, which results in avoidable outages and unhappy customers. The good news is that companies now have the technology to determine potential product outages or failures affecting their customers in real time before they actually happen.
collaboration. Do you know why you need to collaborate and what information you need? When it comes to initiatives such as BigData, BusinessIntelligence
reporting, and Mobilizing, where do you start?
Key Essentials Required During the Planning Stage Considerations in Capturing Source Data
There is much confusion in the market about the current state of data initiatives, especially when it comes to the potential impact of bigdata on traditional businessintelligence and analytics practices. Customers and vendors alike sense a sea-change in the industry as new approaches, technologies, and best practices are rapidly evolving today, but few can accurately assess the motivations, impacts, and implications of the shifts. ESG undertook a broad study to establish a baseline on various data initiatives and their relative maturity.
M.E. Scholar, Dept. of Computer Science and Engineering, UVCE, Bangalore University, Bangalore, India 1 B.E. Student, Dept. of Computer Science and Engineering, UVCE, Bangalore University, Bangalore, India 2
ABSTRACT: Data Warehousing is highly essential for achieving BusinessIntelligence in an Enterprise. A traditional Data Warehouse is built in par with the Inmon’s Architecture which follows Extract, Transform and Load (ETL) strategy for data pre-processing and Online Analytical Processing (OLAP) for Analysis. With several recent trends like the Online Social Networks (OSNs), e-commerce and increasingnumber of internet users, the amount of data has risen exponentially. The Data is highly dynamic where existing Data Warehouse Architectures are unable to keep in par with large amount of data for processing. Though the ETL strategy performs fairly well, it consumes a lot of time for real- time data processing. To enhance the processing capability of large volumes of Data, several BigData Technologies and frameworks are introduced. In this paper, a BigData Oriented Data Warehouse Architecture is proposed where the BigData Technologies are accommodated in the Data Warehouse Architecture in a highly logical manner with an essence of chronological arrangement of the BigData technologies. A detailed Empirical Evaluation of the proposed architecture is conducted based on a survey involving bigdata expertsin order to validate the proposed Data Warehouse Architectureincorporating BigData Technologies. Incorporation of Intelligent and Semantic agents is also achieved for customizing and making the Analysis of Enterprise Level data more efficientand in turn paving a way for improved BusinessIntelligence at the Enterprise Level.
Technology Innovations for Enhanced Database Management and Advanced BI
Copyright 2013 BI Research and Intelligent Solutions, Inc., All Rights Reserved. 4 The requirements shown in Figure 2 cannot be satisfactorily met by the traditional EDW.
For example, the more up-to-date the data is in the EDW with respect to operational systems, the better visibility there will be into recent business operations. There is a limit, however, to how up-to-date the data in a data warehouse can be, how fast analytics can be produced and delivered to business users, and how rapidly users can make decisions and take actions. There are inherent latencies in the traditional EDW BI lifecycle, and although these latencies can be reduced, they cannot be eliminated to enable real-time decisions to be made on real-time data. The traditional EDW environment therefore needs to be extended to support RT operational intelligence.