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Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems

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2020, Master of Science in Computer Engineering (MSCE), Wright State University, Computer Engineering.
Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide a way to combine (interlink and integrate) data streams in different modalities in a meaningful way, with the in-depth use case of flood events. The DisasterRecord project was originally developed as a demo to showcase the efforts of the team at Kno.e.sis in the area of combining and analyzing multimodal data for the IBM CallForCode challenge in 2018. This thesis represents extensive follow-on work in the areas of deployability, flexibility, and reliability. Specific topics addressed are: a method that utilizes current technologies to easily deploy into cloud infrastructure; the modifications made to add flexibility to add and modify the multimodal analysis pipeline; and reliability improvements to make it a stable and reliable system.
Amit Sheth, Ph.D. (Advisor)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Valerie Shalin, Ph.D. (Committee Member)
47 p.

Recommended Citations

Citations

  • Partin, M. (2020). Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567073723628721

    APA Style (7th edition)

  • Partin, Michael. Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems. 2020. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1567073723628721.

    MLA Style (8th edition)

  • Partin, Michael. "Scalable, Pluggable, and Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems." Master's thesis, Wright State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567073723628721

    Chicago Manual of Style (17th edition)