Big Data Analysis

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Contents

Research

Information Integration and Data Mining

Heterogeneity and Complex interdependence
Heterogeneity and Complex interdependence

The major challenge with Big Data analysis is attributed to varying granularity, incompatible data models, and complex interdependence across content. Existing frameworks for data analysis support specific types of data (for example, real-time streams, social content) and assume homogeneous computing resources which is rarely the case in real-world complex systems.

We are developing a framework for rapid integration of heterogeneous Big Data information sources. The framework captures complex interrelationships and interdependence across datasets and establishes probabilistic linkages among distributed content. The system is built using Semantic Web technologies, facilitating complex queries to be issued across the integrated data repositories. This approach complements existing techniques by providing probabilistic queries that take into account the discovered structure among the data sources. To facilitate rich analysis of the integrated datasets, we leverage existing statistical learning, machine learning, and data mining techniques. We are also developing algorithms that identify both simple and complex patterns across datasets. Such patterns are also used to improve the integration process.

Automation achieved in this manner not only reduces the manual effort involved in cleansing and processing large datasets significantly, but also ensures consistency and effective use of compute and storage resources. The framework is being validated on real-world use-cases from the petroleum industry.


Complex Event Modeling and Management

event pattern detection
Event Pattern Detection

Complex event processing (CEP) involves detection and analysis of incidents in time-series. Semantic web techniques provide a new dimension to CEP by incorporating interoperability, richer expressivity, and reasoning.

We are developing a semantic process-oriented event model that captures the complete lifecycle of an event from instrumentation, detection, analysis and action to event correlation and prediction. This facilitates effective communication across all software systems involved. The model also incorporates learning of complex event patterns from historical data for event detection and prediction. We are applying these models to large spatio-temporal streaming data from different domains. In particular, we have applied this event processing architecture in smart oilfields to facilitates enterprise information integration.


Collaboration Analytics

Predicting Communication intention
Predicting Communication intention

The vast scale (Big data) of online human interactions impose challenges to the study of interdisciplinary theories that describe collaboration, which are by their nature intertwined in multiple dimensions. This research focuses on modeling such multidimensional data, mining their intra and inter dependencies to uncover hidden structures and emergent knowledge. In particular, we examine informal interactions at the workplace as well as online social media. We study users’ communication behavioral patterns, dynamics and characteristics, statistical properties and complex correlations between social and topical structures. We have performed quantitative studies of communication patterns in a corporate microblogging service. Our analysis suggests that users with strong local topical alignment tend to participate in focused interactions, whereas users with disperse interests contribute to multiple discussions, broadening the diversity of participants.

We are also developing models for predicting communication intention, recipient recommendation in microblogging services, collective opinion mining and sentiment analysis in social media, and expertise identification from heterogeneous datasets.



Team Members

Alumni

Publications

  1. Big Data Analytics for Demand Response: Clustering Over Space and Time. Charalampos Chelmis, Jahanvi Kolte and Viktor K. Prasanna, IEEE International Conference on Big Data Workshopspdf]
  2. Multivariate Time Series Classification Using Inter-leaved Shapelets. Om Patri, Rajgopal Kannan, Anand Panangadan, and Viktor K. Prasanna, NIPS Time Workshop 2015, Montreal, QC, Canada [pdf]
  3. Personalized Trip Planning by Integrating Multimodal User-generated Content. Om Patri, Ketan Singh, Pedro Szekely, Anand Panangadan, and Viktor K. Prasanna, IEEE International Conference on Semantic Computing, ICSC 2015 [pdf]
  4. Extracting Discriminative Shapelets from Heterogeneous Sensor Data. Om Patri, Abhishek Sharma, Haifeng Chen, Guofei Jiang, Anand Panangadan and Viktor K. Prasanna, IEEE International Conference on Big Data, IEEE BigData 2014 [pdf]
  5. The Unified Model of Social Influence and its Application in Influence Maximization. Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna, Social Network Analysis and Mining (2015) [pdf]
  6. Predicting Compressor Valve Failures from Multi-Sensor Data. Om Patri, Nabor Reyna, Anand Panangadan, Viktor K. Prasanna, Society of Petroleum Engineers Western Regional Meeting, SPE WRM 2015 [pdf]
  7. Learning of Performance Measures from Crowd-sourced Data with Application to Ranking of Investments. Greg Harris, Anand Panangadan, Viktor K. Prasanna, The 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2015, Ho Chi Minh City, Vietnam [pdf]
  8. Social Influence Computation and Maximization in Signed Networks with Competing Cascades. Ajitesh Srivastava, Charalampos Chelmis and Viktor K. Prasanna,The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France. [pdf]
  9. UFOMQ: An Algorithm for Querying for Similar Individuals in Heterogeneous Ontologies. Yinuo Zhang, Anand Panangadan, Viktor K. Prasanna, The 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWak) 2015, Valencia, Spain. [pdf]
  10. Integration of Heterogeneous Web Services for Event-based Social Networks. Yinuo Zhang, Hao Wu, Anand Panangadan, Viktor K. Prasanna, The 16th IEEE International Conference on Information Reuse and Integration (IEEE IRI) 2015, San Francisco, California. [pdf]
  11. Computational Cost of Querying for Related Entities in Different Ontologies. Chung Ming Cheung, Yinuo Zhang, Anand Panangadan, Viktor K. Prasanna, The 16th IEEE International Conference on Information Reuse and Integration (IEEE IRI) 2015, San Francisco, California. [pdf]
  12. FP-CPNNQ: A Filter-Based Protocol for Continuous Probabilistic Nearest Neighbor Query. Yinuo Zhang, Anand Panangadan, Viktor K. Prasanna, The 20th International Conference on Database Systems for Advanced Applications (DASFAA) 2015, Hanoi, Vietnam. [pdf]
  13. Semantic web technologies for external corrosion detection in smart oil fields. Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K. Prasanna, Robert House, Jacques Blouin and Brian Thigpen (SPE Western Regional Meeting) 2015, Garden Grove, California, USA. [pdf]
  14. Interactive Query Refinement for Boolean Search. Greg Harris, Anand Panangadan, and Viktor K. Prasanna, Semantic Analysis of Documents Workshop (SemADoc 2014) [pdf]
  15. Peer Review in Online Forums: Classifying Feedback-Sentiment. Greg Harris, Anand Panangadan, and Viktor K. Prasanna, Proceedings of the 15th IEEE International Conference on Information Reuse and Integration (IEEE IRI 2014) [pdf]
  16. Predicting Failures from Oilfield Sensor Data using Time Series Shapelets, Om Patri, Anand Panangadan, Charalampos Chelmis, Randall McKee, Viktor K. Prasanna, 91st Society of Petroleum Engineers Annual Technical Conference and Exhibition, SPE ATCE 2014 [pdf]
  17. Extracting Discriminative Features for Event-based Electricity Disaggregation, Om Patri, Anand Panangadan, Charalampos Chelmis, Viktor K. Prasanna, 2nd IEEE Conference on Technologies for Sustainability, SusTech 2014 [pdf]
  18. The Process-oriented Event Model (PoEM) -- A Conceptual Model for Industrial Events, Om Patri, Vikram Sorathia, Anand Panangadan, Viktor K. Prasanna, 8th ACM International Conference on Distributed Event-Based Systems, DEBS 2014 [pdf] (US Patent Application No. 61/765,577, filed Feb 2014)
  19. Semantic Management of Enterprise Integration Patterns: A Use Case in Smart Grids, Om Patri, Anand Panangadan, Vikram Sorathia, and Viktor K. Prasanna, 10th Workshop on Information Integration on the Web, IIWeb 2014, held at the 30th IEEE International Conference on Data Engineering, ICDE 2014 [pdf]
  20. UFOM: Unified Fuzzy Ontology Matching, Yinuo Zhang, Anand Panangadan, Viktor K. Prasanna, The IEEE International Conference on Information Reuse and Integration (IRI 2014). [pdf]
  21. Influence in Social Networks: A Unified Model?, A. Srivastava, C. Chelmis, V. K. Prasanna, The IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2014).
  22. Computational Models of Technology Adoption at the Workplace, C. Charalampos, A. Srivastava, and V. K. Prasanna, Social Network Analysis and Mining 4, no. 1 (2014): 1-18. [url]
  23. Semantic Social Network Analysis for the Enterprise, Charalampos Chelmis, Hao Wu, Vikram Sorathia, Viktor K. Prasanna, Journal of Computing and Informatics - Special Issue on Computational Intelligence for Business Collaboration, 2014 (To Appear)
  24. Social Link Prediction in Online Social Tagging Systems, Charalampos Chelmis, Viktor K. Prasanna, ACM Transactions on Information Systems, 2013 (To Appear). [pdf]
  25. The Role of Organization Hierarchy in Technology Adoption at the Workplace, Charalampos Chelmis, Viktor K. Prasanna, The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, August 2013. [pdf]
  26. Complex Modelling and Analysis of Workplace Collaboration Data, Charalampos Chelmis, International Conference on Collaboration Technologies and Systems, 2013. [pdf]
  27. Enterprise Knowledge Preservation and Management, Charalampos Chelmis, Vikram Sorathia, Viktor K. Prasanna, Book Chapter, Collaborative Processes and Decision Making in Organizations, IGI Global, 2013 <doi>
  28. An Empirical Analysis of Microblogging Behavior in the Enterprise, Charalampos Chelmis, Viktor K. Prasanna, Social Networking Analysis and Mining, Springer Wien, 2013. <doi>
  29. Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media, Charalampos Chelmis, Viktor K. Prasanna, 4th ACM International workshop on Modeling social media, May 2013. [pdf]
  30. Event Recommendation in Social Networks with Linked Data Enablement. Yinuo Zhang, Hao Wu, Vikram Sorathia and Viktor K. Prasanna, the 9th International Conference on Enterprise Information Systems (ICEIS), July 2013. [pdf]
  31. Toward an Automatic Metadata Management Framework for Smart Oil Fields. Charalampos Chelmis, Jing Zhao, Vikram Sorathia, Agarwal Suchindra, Viktor K. Prasanna, SPE Economics & Management, 5(1):33-43, 2013. [pdf]
  32. Enriching Employee Ontology for Enterprises with Knowledge Discovery from Social Networks, Hao Wu, Charalampos Chelmis, Yinuo Zhang, Vikram Sorathia, Om Patri, Viktor K. Prasanna, The 3rd Workshop on Social Network Analysis in Applications, August 2013. [pdf]
  33. A Bag-of-semantics Model for Image Clustering, Na Chen and Viktor K. Prasanna, The Visual Computer journal, 2013.
  34. Event-driven Information Integration for the Digital Oilfield, Om Patri, Vikram Sorathia and Viktor K. Prasanna, 89th Society of Petroleum Engineers Annual Technical Conference and Exhibition, SPE ATCE 2014 [pdf] (US Patent Application No. 13/492,430, filed June 2012)
  35. Semantic Image Clustering Using Object Relation Network, Na Chen and Viktor K. Prasanna, to appear in Proceedings of the Computational Visual Media Conference, 2012. [pdf]
  36. Learning to Rank Complex Semantic Relationships, Na Chen and Viktor K. Prasanna, to appear in International Journal on Semantic Web and Information Systems, Dec 2012. [pdf]
  37. Rankbox: An Adaptive Ranking System for Mining Complex Semantic Relationships Using User Feedback, Na Chen and Viktor K. Prasanna, in Proceedings of 13th IEEE International Conference on Information Reuse and Integration, August, 2012. [pdf]
  38. Understanding Web Images by Object Relation Network, Na Chen, Qian-Yi Zhou and Viktor K. Prasanna, in Proceedings of the 21th international conference on World Wide Web, April, 2012. [pdf]
  39. Recovering Linkage Between Seismic Images and Velocity Models, Jing Zhao, Charalampos Chelmis, Vikram Sorathia, Viktor K. Prasanna, Aman Goel, SPE Western Regional Meeting, March, 2012. [docx]
  40. Semiautomatic, Semantic Assistance to Manual Curation of Data in Smart Oil Fields, Charalampos Chelmis, Jing Zhao, Vikram Sorathia, Suchindra Agarwal, Viktor K. Prasanna, SPE Western Regional Meeting, March, 2012. [pdf]
  41. When Diversity Meets Speciality: Friend Recommendation in Online Social Networks, Hao Wu, Vikram Sorathia and Viktor K. Prasanna, ASE Human Journal, Vol 1, No 1, 2012. [pdf]
  42. Predict Whom One Will Follow: Followee Recommendation in Microblogs, Hao Wu, Vikram Sorathia and Viktor K. Prasanna, ASE International Conference on Social Informatics (SocialInfo), December, 2012. [pdf]
  43. Predicting Communication Intention in Social Networks, Charalampos Chelmis and Viktor K. Prasanna, ASE/IEEE International Conference on Social Computing (SocialCom), September, 2012. [pdf]
  44. Microblogging in the Enterprise: A few comments are in order, Charalampos Chelmis and Viktor K. Prasanna, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August, 2012. [pdf]
  45. Enterprise Wisdom Captured Socially, Charalampos Chelmis, Vikram Sorathia and Viktor K. Prasanna, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August, 2012. [pdf]
  46. Social Networking Analysis: A State of the Art and the Effect of Semantics, Charalampos Chelmis and Viktor K. Prasanna, IEEE Third International Conference on Social Computing (SocialCom), October, 2011. [pdf]
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