Theses and Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/1

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    Pattern based partitioning and distribution for sensor data
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2017) Mulla, Zubain; Bhise, Minal
    "Nowadays there has been a rapid growth in the number of RDF data based applications. The data requests are of categories like real time request, critical contents, swift analysis etc. Query Processing Time is a very big factor in deciding the performance for such applications based over the triple structure of RDF i.e. subject, property and object. These application’s data-sets are very vast in terms of size and needs a redesign. The distributed architecture helps in utilizing the memory as well as storage limits in an efficient manner. The processing power available from different computing machines in the distributed architecture helps in solving such mentioned requests simultaneously without adding load only to a single sever. We will be utilizing a distributed architecture to translate the given RDF dataset into a relational schema to enhance query processing. The findings in the thesis will help in accelerating the query processing time contributing towards faster speed of forecasting of weather with the aid of sensor data. It will help in avoidance and prevention of calamities and also aid to the improvement in weather forecast methods."
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    Log based method for faster IoT queries
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2017) Jain, Anubha; Bhise, Minal
    With increase in the size of Internet of Things IoT device networks and applications, tremendous increase is witnessed in the size of IoT data. To build smart applications using IoT data, its efficient storage is important to facilitate faster queries. IoT data is represented in Resource Description Framework RDF and stored in relational format. This thesis addresses the issue of faster query processing of this data. It resents a Log Based Method LBM to partition IoT data. IoT systems exhibit skewedness in data access patterns as some records are accessed more frequently than the other. LBM exploits this skewedness in access patterns of records. It incorporates Forward Algorithm FA and Backward Algorithm BA to analyse the query workload and partition the basic triple table into hot and cold data tables. For our experiment, 8% of hot data table is found to solve 78.6% queries. The query execution is found to be 67.5% faster on partitioned data than triple table. To further accelerate FA and BA, we have executed them in parallel as well which is found to be 42% faster than its serial execution.
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    SPARQLGen: generation of SPARQL from pseudo BGP
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Mandloi, Dipendra Singh; Chaudhary, Sanjay; Jat, Pokhar Mal
    SPARQL is the querying language and communication protocol for communicating with RDF data sources. SPARQL query requires knowledge of URIs of bound values in the triple patterns and ontological schema used by dataset. A person, even expert in SPARQL, nds hard to gure out URIs for bound values to be used in the query. This requirement brings a gap between end user and SPARQL query formation. In this work, we aim to facilitate semantic search over web of data by converting keywords into URIs, and present SPARQLGen. SPARQLGen provides an easy way of writing SPARQL query for a given query over Web of Data (RDF data). Through appropriate interface, semantic annotations of keywords are captured. We derive a Pseudo Basic Graph Pattern which is basically similar to SPARQL BGP except that it contains keywords rather than full resource URIs. Here, we propose heuristics that discover URIs for annotated keywords and build corresponding SPARQL query. SPARQLGen takes services of falcons, a semantic search engine. The Linked Open Data plays the major role in nding aliased URIs of an entity. The nal set of results contains a list of URIs of different data sources. SPARQLGen bridges the gap between end user and SPARQL query formation. The interface allows users to write user intended keywords instead of highly syntactic SPARQL query so that he/she needs not worry about the URIs of entities while writing their queries.