NOUS: Construction and Querying of Dynamic Knowledge Graphs

The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU

Mining emerging massive scientific sequence data using block-wise decomposition methods

I present efficient data mining algorithms for knowledge discovery on two types of emerging large-scale sequence-based scientific datasets: 1) static sequence data generated from SNP diversity arrays for genomic studies, and 2) dynamic sequence data collected in streaming and sensor network systems for environmental studies. The massive, noisy nature of the SNP arrays and the distributive, online nature of sensor network data pose challenging issues for knowledge discovery such as scalability, robustness, and efficiency. Despite the different characteristics of the SNP arrays and streaming sensor data, when viewed as sequences of ordered observations, both can be efficiently mined using algorithms based on block-wise decomposition methods. I present models and mining algorithms for inferring the genetic variation structure in genome-wide Single-Nucleotide Polymorphism (SNP) arrays. Genome-wide SNP arrays provide a comprehensive view of genome variation and serve as powerful resources for genetic and biomedical studies. Understanding the patterns of genetic variation in a population of individuals plays an important role in solving many genetics problems such as genealogy reconstruction and gene association studies. In this thesis, I propose data mining models and algorithms to efficiently infer genetic variation structure from the massive SNP panels of recombinant sequences resulting from meiotic recombination. I introduced the Minimum Segmentation Problem (MSP) to infer the segmentation structure of a single recombinant strain, as well as the Minimum Mosaic Problem (MMP) to infer the mosaic structure on a panel of recombinant strains. Both MSP and MMP estimate the ancestral polymorphism patterns exhibited in recombinant strains which provides important inputs for the subsequent association analysis. Efficient dynamic programming and graph algorithms based on block-wise decomposition are proposed which can solve MSP and MMP on genome-wide large-scale panels. I present efficient algorithms for mining massive streaming and sensor network data for observational sciences such as ecology and environmental studies. I proposed efficient algorithms using block-wise synopsis construction to capture the data distribution online for the dynamic sequence data collected in the sensor network and streaming systems including clustering analysis and order-statistics computation, which is critical for real-time monitoring, anomaly detection, and other domain specific analysis

Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio