Business Intelligence A Managerial Perspective on Analytics

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Business Intelligence A Managerial Perspective on Analytics
Business Intelligence A Managerial Perspective on Analytics
maintained at a temperature of 35-46 degrees Fahrenheit (2-8 degrees Centigrade). Maintaining cold chain integrity is extremely important for healthcare product manufacturers.
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Erik Brynjolfsson
In today's world, as the volume of business and consumer data continues to grow at an unprecedented pace, there is increasing desire to utilize that data in new and innovative and ways to provide insight and improve decision making. For businesses, data is being generated from transactions, machine logs, digital media and feeds from sensors and wireless devices at a volume and velocity not seen before.
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Parvaiz koul , Syed Jamaluddin Ahmad
To study the uptake of influenza vaccination among pregnant women in northern India and physicians' beliefs and practices regarding vaccination. A questionnaire-based survey was undertaken between October 2012 and April 2013. Pregnant women attending an obstetric hospital in Srinagar, India, and healthcare personnel were asked to participate. Among 1000 women aged 18-41 years (13.6% first trimester, 26.8% second trimester), none had been offered or received influenza vaccination. Only 9 (10.0%) of 90 obstetricians surveyed had been vaccinated for influenza in the past 5 years, although 81 (90.0%) believed that influenza could have severe consequences for themselves and their patients. The reasons cited for non-vaccination included poor knowledge about availability of vaccine and concerns about its efficacy. Sixty-six (73.3%) obstetricians believed that vaccine adverse effects are under-reported, and 79 (87.8%) believed that vaccination programs are motivated by profit. Eighty-fo...
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John Ooko
A lot has happened in the worlds of Big Data, NoSQL and Cloud Computing in the past year alone. There is the new architecture of Hadoop that has made it more usable. NoSQL databases like HBase, Cassandra and MongoDB have taken on new features. Then there is the Amazon's DynamoDB which has become far more capable in terms of indexing and its integration with other AWS data services. Microsoft has introduced its own NoSQL database called DocumentDB and added HBase to HDInsight, which is its Hadoop distribution. HDInsight now runs on Linux as well as Windows. The big question is what is next and how do all these impact you and me?
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Anand Pathak
This paper reviews various approaches to infer the patterns from Big Data using aggregation, filtering and tagging. Earlier research shows that data aggregation concerns about gathered data and how efficiently it can be utilized. It is understandable that at the time of data gathering one does not care much about whether the gathered data will be useful or not. Hence, filtering and tagging of the data are the crucial steps in collecting the relevant data to fulfill the need. Therefore the main goal of this paper is to present a detailed and comprehensive survey on different approaches. To make the concept clearer, we have provided a brief introduction of Big Data, how it works, working of two data aggregation tools (namely, flume and sqoop), data processing tools (hive and mahout) and various algorithms that can be useful to understand the topic. At last we have included comparisons between aggregation tools, processing tools as well as various algorithms through its pre-process, ma...
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Kyar Nyo Aye
Data continue a massive expansion in scale, diversity, and complexity. Data underpin in all sectors of society. Achieving the full transformative potential from the use of this massive data in increasingly digital world requires not only new data analysis algorithms but also a new generation of distributed computing platforms. Big data analytics is an area of rapidly growing diversity. It requires massive performance, scalability and fault tolerance. Existing big data platforms cannot scale to big data volumes, cannot handle mixed workloads, cannot respond to queries quickly, load data too slowly and lack processing capacity for analytics. Traditional data warehousing is a large but relatively slow producer of information to analytics users and mostly ideal for analyzing structured data from various systems. Distributed scale-out storage system meets the needs of big data challenges. Hadoop-based platform is well suited to deal with not only structured data but also semi structured and unstructured data, and provides scalability and fault tolerance. Therefore, Hadoop-based platform based on distributed scale-out storage system emerges to deal with big data. However, NameNode in Hadoop is used to store metadata in a single system’s memory, which is a performance bottleneck for scale-out. Gluster file system has no performance bottlenecks related to metadata because it uses an elastic hashing algorithm to place data across the nodes and it runs across all of those nodes. The aim of this research is to propose a big data analytics platform on distributed scale-out storage system to achieve massive performance, scalability and fault tolerance. It consists of two parts: big data processing and big data storage. For big data processing, Hadoop MapReduce is applied to handle mixed workloads, respond analytical queries rapidly and support various high level query languages. For big data storage, Gluster file system is used to achieve better scalability, fault tolerance and faster data loading. The main issue in Gluster file system is inefficient data rebalancing. Therefore, a data rebalancing mechanism for Gluster file system is proposed to achieve efficient storage utilization, to reduce the number of file migrations and to save files migration time. The Hadoop big data platform (MapReduce and Hadoop Distributed File System) and the proposed big data platform (MapReduce and Gluster File System) are implemented on commodity Linux Virtual Machines clusters and performance evaluations are conducted. According to the evaluation analysis, the proposed big data platform provides better scalability, fault tolerance, and faster query response time than the Hadoop platform. According to the simulation results, the proposed data rebalancing mechanism provides 82% (fullness percent), 20% of the number of file migrations, 20% of the files migration time, and 73% of the number of required storage servers of the current mechanism of Gluster file system.
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