Agile Big Data Analytics for Web-Based Systems: An Architecture-Centric Approach

Abstract

This article contributes an architecture-centric methodology, called AABA (Architecture-centric Agile Big data Analytics), to address the technical, organizational, and rapid technology change challenges of both big data system development and agile delivery of big data analytics for Web-based Systems (WBS). As the first of its kind, AABA fills a methodological void by adopting an architecture-centric approach, advancing and integrating software architecture analysis and design, big data modeling and agile practices. This article describes how AABA was developed, evolved and validated simultaneously in 10 empirical WBS case studies through 3 CPR (Collaborative Practice Research) cycles. In addition, this article presents an 11th case study illustrating the processes, methods and techniques/tools in AABA for costeffectively achieving business goals and architecture agility in a large scale WBS. All 11 case studies showed that architecturecentric design, development, and operation is key to taming technical complexity and achieving agility necessary for successful WBS big data analytics development. Our contribution is novel and important. The use of reference architectures, a design concepts catalog and architectural spikes in AABA are advancements to architecture design methods. In addition, our architecture-centric approach to DevOps was critical for achieving strategic control over continuous big data value delivery for WBS.

Can Cybersecurity Be Proactive? A Big Data Approach and Challenges

“…Interestingly, we have noted that both source types recognize occurrences of PoC exploits, attacks, and vulnerabilities at the same time. It has been shown that there are time delays, e.g., the Germination Period, both between the identification of vulnerabilities and the production of PoC exploits and also between hostile attacks targeting these vulnerabilities and the corresponding PoC exploits [6] [23]. We intend to broaden these data sources, thus taking a big data approach, to show something more general, by identifying as many contexts as possible and determining whether the time delays are different in different contexts.…”

“…Large volumes and different varieties of data will have to be collected from the main data sources, ingested, stored and prepared for analysis. A big data repository is thus planned for storing the raw data to allow "schema on read" [5] [6] for different types of analysis. There are tremendous technical challenges in terms of preparing data for analysis.…”

“…There are tremendous technical challenges in terms of preparing data for analysis. The data cleaning and integration is not a trivial task [6].…”

Big Data and software engineering: prospects for mutual enrichment

“…Chen et al [15] propose an architecture-centric methodology to address the technical, organizational, and rapid technology change challenges of Big Data system development for Big Data Web analytics using an agile approach. This architecture-centric agile approach was able to cost-effectively achieve business goals and architecture agility in 11 Web-based systems presented by the authors.…”

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

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