Big Data Engineering Minitrack

Permanent URI for this collection

https://hdl.handle.net/10125/42602

This minitrack will cover advances in the broad range of activities that are required to cost-effectively plan, design, build, evolve and manage big data systems. The sheer scale and pace of change mandates the computer science and software engineering communities develop new methods and tools to plan, design, build, evolve and manage big data systems that derive on-going business value. Big Data Engineering (BDE) provide a focus for the synergies that must be developed across technical disciplines to reliably deliver production- level applications that handle unprecedented amounts of data, new variety of data type and real time velocity.

To this end, this minitrack will solicit papers in the following areas:

  • New system architectures and methods for big data sourcing/harnessing, ingestion, storage, processing, exploration, analysis and visualization.
  • Methods for integration, interoperability and metadata modeling for big data access.
  • Hybrid architectures/models for big data systems coexisting with traditional data warehouses
  • Software engineering methods and decision support tools for massively scalable systems development
  • Advanced technologies: MPP, NoSQL databases, real time stream processing, in-memory processing frameworks, scalable analytics, big data cloud technologies and platforms,
  • Engineering methods for generating and facilitating innovation and/or new business models from big data collections and analytics.
  • New programming models and languages for big data
  • Engineering approaches for big data governance, compliance, privacy and security
  • Novel big data engineering approaches in specific application domains.
  • Other relevant topics related to big data engineering such as education/curriculum issues for producing big data engineers.

Minitrack Co-Chairs:

Hong-Mei Chen (Primary Contact)
University of Hawaii at Manoa
Email: hmchen@hawaii.edu

Ken Anderson
University of Colorado
Email: kena@cs.colorado.edu

Wietske van Osch
Michigan State University
Email: vanosch@msu.edu

Browse

Recent Submissions

Now showing 1 - 3 of 3
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    Big Data Value Engineering for Business Model Innovation
    (2017-01-04) Chen, Hong-Mei; Kazman, Rick; Garbajosa, Juan; Gonzalez, Eloy
    Big data value engineering for business model innovation requires a drastically different approach as compared with methods for engineering value under existing business models. Taking a Design Science approach, we conducted an exploratory study to formulate the requirements for a method to aid in engineering value via innovation. We then developed a method, called Eco-ARCH (Eco-ARCHitecture) for value discovery. This method is tightly integrated with the BDD (Big Data Design) method for value realization, to form a big data value engineering methodology for addressing these requirements. The Eco-ARCH approach is most suitable for the big data context where system boundaries are fluid, requirements are ill-defined, many stakeholders are unknown, design goals are not provided, no central architecture pre-exists, system behavior is non-deterministic and continuously evolving, and co-creation with consumers and prosumers is essential to achieving innovation goals. The method was empirically validated in collaboration with an IT service company in the Electric Power industry.
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    Batch to Real-Time: Incremental Data Collection & Analytics Platform
    (2017-01-04) Aydin, Ahmet; Anderson, Ken
    Real-time data collection and analytics is a desirable but challenging feature to provide in data-intensive software systems. To provide highly concurrent and efficient real-time analytics on streaming data at interactive speeds requires a well-designed software architecture that makes use of a carefully selected set of software frameworks. In this paper, we report on the design and implementation of the Incremental Data Collection & Analytics Platform (IDCAP). The IDCAP provides incremental data collection and indexing in real-time of social media data; support for real-time analytics at interactive speeds; highly concurrent batch data processing supported by a novel data model; and a front-end web client that allows an analyst to manage IDCAP resources, to monitor incoming data in real-time, and to provide an interface that allows incremental queries to be performed on top of large Twitter datasets.
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    Introduction to Big Data Engineering Minitrack
    (2017-01-04) Chen, Hong-Mei; Anderson, Ken; van Osch, Wietske