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The goal of big data analytics systems is to help business decisions supported by pieces of evidence from voluminous and diverse but uncertain data with high processing speed to create value. However, how the goal can be achieved is unclear and involves further exploration. In this paper, we propose a reference model for big data analytics which can help extract business questions and rationally evolve the questions into optimal analytics operationalizations explicitly considering underlying assumptions to achieve big data analytics goals aligned with business goals. It consists of 4 sub-models, Business Question Extraction Model, Big Data Analytics Evolution Model, Analytics Algorithm Reference Model, and Goal-Oriented Optimal Selection Model. We applied our reference model to a shipment decision in the retail business as an empirical study. We compared it with existing solutions in diverse aspects.

Estimating requirements development effort is a critical activity in the software development process. Producing an accurate estimate, however, is extremely difficult, especially in the early stages of the software development process, where no clear and no complete description of the desired software behavior is available, hence leading to an estimate process with a high level of uncertainty. To handle this issue, in this paper, we present REEE (Requirements Elicitation Effort Estimation) – a framework for incrementally estimating software requirements elicitation efforts. We first categorize a project in terms of project’s domain, different types of project parameters and requirements – functional requirements (FRs), non-functional requirements (NFRs), constraints and domain properties (DPs), while taking into consideration relationships among them. We then present an incremental approach to software requirements elicitation effort estimation that uses similar past projects in a case-based reasoning (CBR) model. In this approach, semantic similarity is quantified, once classification and normalization of requirements are done. To see the strengths and limitations of REEE, a dataset of 36(students’) projects have been analyzed. Experimental results show that the use of REEE is a viable alternative to other cost estimation techniques, and can provide better results, with less uncertainty, for requirements elicitation effort estimation.

A business process is a collection of activities to create more business values and its continuous improvement aligned with business goals is essential to survive in fast changing business environment. However, it is quite challenging to find out whether a change of business processes positively affects business goals or not, if there are problems in the changing, what the reasons of the problems are, what solutions exist for the problems and which solutions should be selected. Big data analytics along with a goal-orientation which helps find out insights from a large volume of data in a goal concept opens up a new way for an effective business process reengineering. In this paper, we suggest a novel modeling framework which consists of a conceptual modeling language, a process and a tool for effective business processes reengineering using big data analytics and a goal-oriented approach. The modeling language defines important concepts for business process reengineering with metamodels and shows the concepts with complementary views: Business Goal-Process-Big Analytics Alignment View, Transformational Insight View and Big Analytics Query View. Analyzers hypothesize problems and solutions of business processes by using the modeling language, and the problems and solutions will be validated by the results of Big Analytics Queries which supports not only standard SQL operation, but also analytics operation such as prediction. The queries are run in an execution engine of our tool on top of Spark which is one of big data processing frameworks. In a goal-oriented spirit, all concepts not only business goals and business processes, but also big analytics queries are considered as goals, and alternatives are explored and selections are made among the alternatives using trade-off analysis. To illustrate and validate our approach, we use an automobile logistics example, then compare previous work.

Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this paper, we present IRIS - a goal-oriented big data analytics framework using Spark for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.