Bastani, Farokh B.

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/7146

Farokh Bastani is Professor of Computer Science and Engineering and holds UTD's Excellence in Education Chair, He is also the Director of the UT Dallas National Science Foundation Net-Centric Software & Systems Industry/University Cooperative Research Center. His research interests include:

  • AI-Based Automated Software Synthesis and Testing
  • Embedded Real-Time Process-Control and Telecommunications Systems
  • Formal Methods and Automated Program Transformation
  • High-Assurance Autonomous Decentralized Systems
  • High-Confidence Software Reliability and Safety Assurance
  • Inherently Fault-Tolerant and Self-Stabilizing Distributed Systems
  • Modular Parallel Programs
  • Tele-Collaborative Systems

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Recent Submissions

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    Invited Paper: Semantic IoT Data Description and Discovery in the IoT-Edge-Fog-Cloud Infrastructure
    (Institute of Electrical and Electronics Engineers Inc., 2019-04-04) Zeng, Wenxi; Zhang, Shuai; Yen, I-Ling; Bastani, Farokh B.; Zeng, Wenxi; Zhang, Shuai; Yen, I-Ling; Bastani, Farokh B.
    Many IoT systems are data intensive, where a large volume of data steadily get generated from a large number of sensors in the system. These data are continuous, thus, how to store and manage them is an important issue. Existing time series databases (TSDBs) offer some good strategies for storing continuous IoT data streams, but they lack a good semantic model for describing the IoT data streams to support effective data discovery. This shortcoming becomes critical when we consider the need for data sharing in many application domains; and it becomes significant when we consider the super huge scale of the IoT-Edge-Fog-Cloud infrastructure and the dynamic data flows in the infrastructure. In this paper, we develop the solutions for IoT data management in the IoT-Edge-Fog-Cloud infrastructure. We focus on the issues of data storage, specification and discovery. First, we build a semantic model for better specification of the IoT data streams (time series data), the DS-ontology. We have applied DS-ontology to TSDBs and developed the SE-TSDB tool suite, which runs on top of existing TSDBs to help establish semantic specifications for data streams and enable semantic-based data retrievals. We have also developed the IoT data discovery techniques based on SE-TSDB to facilitate semantic based data retrieval in the IoT-Edge-Fog-Cloud infrastructure. With our techniques, IoT data streams can be more effectively tracked and flexibly retrieved to help with integrated data analytics and improved knowledge discovery. © 2019 IEEE.
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    Semantically Enhanced Time Series Databases in IoT-Edge-Cloud Infrastructure
    (IEEE Computer Society) Zhang, Shuai; Zeng, Wenxi; Yen, I-Ling; Bastani, Farokh B.; Bastani, Farokh B.
    Many IoT systems are data intensive and are for the purpose of monitoring for fault detection and diagnosis of critical systems. A large volume of data steadily come out of a large number of sensors in the monitoring system. Thus, we need to consider how to store and manage these data. Existing time series databases (TSDBs) can be used for monitoring data storage, but they do not have good models for describing the data streams stored in the database. In this paper, we develop a semantic model for the specification of the monitoring data streams (time series data) in terms of which sensor generated the data stream, which metric of which entity the sensor is monitoring, what is the relation of the entity to other entities in the system, which measurement unit is used for the data stream, etc. We have also developed a tool suite, SE-TSDB, that can run on top of existing TSDBs to help establish semantic specifications for data streams and enable semanticbased data retrievals. With our semantic model for monitoring data and our SETSDB tool suite, users can retrieve non-existing data streams that can be automatically derived from the semantics. Users can also retrieve data streams without knowing where they are. Semantic based retrieval is especially important in a largescale integrated IoT-Edge-Cloud system, because of its sheer quantity of data, its huge number of computing and IoT devices that may store the data, and the dynamics in data migration and evolution. With better data semantics, data streams can be more effectively tracked and flexibly retrieved to help with timely data analysis and control decision making anywhere and anytime. ©2019 IEEE.

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