Modeling and Querying Business Data with Artifact Lifecycle

Abstract

Business data has been one of the current and future research frontiers, with such big data characteristics as high-volume, high-velocity, high-privacy, and so forth. Most corporations view their business data as a valuable asset and make efforts on the development and optimal utilization on these data. Unfortunately, data management technology at present has been lagging behind the requirements of business big data era. Based on previous business process knowledge, a lifecycle of business data is modeled to achieve consistent description between the data and processes. On this basis, a business data partition method based on user interest is proposed which aims to get minimum number of interferential tuples. Then, to balance data privacy and data transmission cost, our strategy is to explore techniques to execute SQL queries over encrypted business data, split the computations of queries across the server and the client, and optimize the queries with syntax tree. Finally, an instance is provided to verify the usefulness and availability of the proposed method.

Framework for the Implementation of a Big Data Ecosystem in Organizations

“…Based on the characteristics of collected data, different methods and technologies can be applied to discover knowledge (Gandomi and Haider, 2015). Organizations are viewing Big Data as a valuable asset and a source of competitive advantage in many business settings (Schermann et al, 2014;Shao and Lin, 2016), and they are making important efforts on the development and optimal utilization of Big Data technologies in order to take the appropriate decisions (Zhao et al, 2015). Big Data technologies allow monitoring of key factors for strategic decisions, such as customer opinions about a product, service or company, by mining social media data .…”