Modeling software engineering projects as a business: A business intelligence perspective

Hans, Robert T.; Mnkandla, Ernest

doi:10.1109/afrcon.2013.6757849

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The main goal of a BI system is to allow the easy interpretation of these data by identifying new opportunities and implementing effective strategies based on insights that has an impact on the organization's operations by reducing cost, enhance sales, improve operations or any positive factor; Business managers and decisions makers are always under pressure to sustain their organizations' competitiveness and make the right decisions in challenging business environments. Business managers make use of BI tools to assist them make intelligent decisions and understand their business situations [6].…”

Section: Business Intelligencementioning

confidence: 99%

“…The replication factor was set to 2, the client machine that wants to write the data block (split) into HDFS consults the NameNode and receives a list of two DataNodes locations to copy the data block into them, then the client writes the block directly to the DataNode and the receiving DataNode will replicate the data block to the other DataNode, this process will be repeated until there is no blocks remaining in the input data set, when the two DataNodes receive a data block they will send a block received report to the NameNode and a success message to indicate the arrival of the data block and close the session, so the client will be ready to start the process again for the next data block and so on, the NameNode will keep track of each block and knows where to find it in which DataNode, this information will be stored in the NameNode as metadata, the process of loading the data set into HDFS will the same every time new data sets enter the system, this process can be described by the flowchart in Figure (6).…”

Section: Loading the Big Data Set Into Hdfs Stagementioning

confidence: 99%

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Proposed Business Intelligence Systemthrough Big Data

Abdullah¹,

Neama²

2016

ETJ

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Every company or institution in the world has huge amounts of raw data. Since, we are living now in the era of data and data explosion, data is generated in an alarming rates. As the Big Data problem emerges, big data cannot be processed by traditional systems due to its huge size, complexity and rapid generation, and since data became the most important element in the business world to drive companies and institutions in the right direction. Business Intelligence Systems are built to serve that purpose. This paper introduces a system that will implement a Business Intelligence technique to handle Big Data problem through Hadoop Framework benefiting from its functionalities to provide a parallel processing environment by implementing a cluster of three nodes that will hold the data set and running queries on it in parallel using the functionality of Map Reduce algorithm. The system consists of four primary stages: first is the stage of loading the data into the cluster, second is the stage of constructing the data warehouse to become the source layer for the data analysis stage to extract the business insights. The third stage analyzes the data and answers the business problems. Finally the fourth stage is the data visualization stage where the answers that gathered from the previous stage will take the form of visual charts and graphs that will be contained in a unified business intelligence dashboard that will provide the overall look for the business operations.

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Section: Business Intelligencementioning

confidence: 99%

Section: Loading the Big Data Set Into Hdfs Stagementioning

confidence: 99%

Proposed Business Intelligence Systemthrough Big Data

Abdullah¹,

Neama²

2016

ETJ

View full text Add to dashboard Cite

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A descriptive analytics tool for improving project human resource management: The importance of discerning a project team member's sentiments

Hans

Mnkandla

2017

2017 Ieee Africon

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Data Mining Framework for Generating Sales Decision Making Information Using Association Rules

Humayun

2016

ijacsa

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Abstract-The rapid technological development in the field of information and communication technology (ICT) has enabled the databases of super shops to be organized under a countrywide sales decision making network to develop intelligent business systems by generating enriched business policies. This paper presents a data mining framework for generating sales decision making information from sales data using association rules generated from valid user input item set with respect to the sales data under analysis. The proposed framework includes super shop's raw database storing sales data collected through sales application systems at different Point of Sale (POS) terminals. Apriori algorithm is famous for association rule discovery from the transactional database. The proposed technique using customized association rule generation and analysis checks the input items with sales data for validation of the input items. The support and confidence of each rule are computed. Sales decision making information about input items is generated by analyzing each of the generated association rules, which can be used to improve sales decision making policy to attract customers in order to increase sales. It is hoped that this approach for generating sales decision making information by analyzing sales data using association rules is more specific decision and application oriented as the business decision makers are not usually interested to all of the items of the sales database for making a specific sales decision.

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Modeling software engineering projects as a business: A business intelligence perspective

Cited by 3 publications

References 25 publications

Proposed Business Intelligence Systemthrough Big Data

Proposed Business Intelligence Systemthrough Big Data

A descriptive analytics tool for improving project human resource management: The importance of discerning a project team member's sentiments

Data Mining Framework for Generating Sales Decision Making Information Using Association Rules

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