Browsing by Author "Jamadagni, N."

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    GoDB: From Batch Processing to Distributed Querying over Property Graphs
    (2016) Jamadagni, N.; Simmhan, Y.
    Property Graphs with rich attributes over vertices and edges are becoming common. Querying and mining such linked Big Data is important for knowledge discovery and mining. Distributed graph platforms like Pregel focus on batch execution on commodity clusters. But exploratory analytics requires platforms that are both responsive and scalable. We propose Graph-oriented Database (GoDB), a distributed graph database that supports declarative queries over large property graphs. GoDB builds upon our GoFFish subgraph-centric batch processing platform, leveraging its scalability while using execution heuristics to offer responsiveness. The GoDB declarative query model supports vertex, edge, path and reachability queries, and this is translated to a distributed execution plan on GoFFish. We also propose a novel cost model to choose a query plan that minimizes the execution latency. We evaluate GoDB deployed on the Azure IaaS Cloud, over real-world property graphs and for a diverse workload of 500 queries. These show that the cost model selects the optimal execution plan at least 80% of the time, and helps GoDB weakly scale with the graph size. A comparative study with Titan, a leading open-source graph database, shows that we complete all queries, each in &x2264; 1:6 secs, while Titan cannot complete up to 42% of some query workloads. � 2016 IEEE.
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    GoDB: From Batch Processing to Distributed Querying over Property Graphs
    (Institute of Electrical and Electronics Engineers Inc., 2016) Jamadagni, N.; Simmhan, Y.
    Property Graphs with rich attributes over vertices and edges are becoming common. Querying and mining such linked Big Data is important for knowledge discovery and mining. Distributed graph platforms like Pregel focus on batch execution on commodity clusters. But exploratory analytics requires platforms that are both responsive and scalable. We propose Graph-oriented Database (GoDB), a distributed graph database that supports declarative queries over large property graphs. GoDB builds upon our GoFFish subgraph-centric batch processing platform, leveraging its scalability while using execution heuristics to offer responsiveness. The GoDB declarative query model supports vertex, edge, path and reachability queries, and this is translated to a distributed execution plan on GoFFish. We also propose a novel cost model to choose a query plan that minimizes the execution latency. We evaluate GoDB deployed on the Azure IaaS Cloud, over real-world property graphs and for a diverse workload of 500 queries. These show that the cost model selects the optimal execution plan at least 80% of the time, and helps GoDB weakly scale with the graph size. A comparative study with Titan, a leading open-source graph database, shows that we complete all queries, each in &x2264; 1:6 secs, while Titan cannot complete up to 42% of some query workloads. © 2016 IEEE.
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    O3 - A webpage preprocessing tool
    (2015) Senthil, K.; Bhat, K.S.; Jamadagni, N.; Sureshan, S.; Prasad, G.
    One of the prime factors for the success of the internet is determined by the time taken to load a web page. Even a difference of a few hundred milliseconds in the response time will largely affect the number of users of a web page to shift from one to the other. So, in the commercial market, providing quick service to the users is of utmost importance in remaining ahead of competitors. In this paper, we mainly address this issue by applying various optimization techniques at the front-end to improve the user experience by reducing the load time of the web pages. Though the overall optimization is purely web page-dependent, the optimization techniques not only reduce the time taken to load the page, but also reduce the load on the server.
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    O3 - A webpage preprocessing tool
    (SciTePress, 2015) Senthil, K.; Bhat, K.S.; Jamadagni, N.; Sureshan, S.; Prasad, G.
    One of the prime factors for the success of the internet is determined by the time taken to load a web page. Even a difference of a few hundred milliseconds in the response time will largely affect the number of users of a web page to shift from one to the other. So, in the commercial market, providing quick service to the users is of utmost importance in remaining ahead of competitors. In this paper, we mainly address this issue by applying various optimization techniques at the front-end to improve the user experience by reducing the load time of the web pages. Though the overall optimization is purely web page-dependent, the optimization techniques not only reduce the time taken to load the page, but also reduce the load on the server.