Mathematics (Faculty of)

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Browsing Mathematics (Faculty of) by Author "Aboulnaga, Ashraf"

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    Analytics for Everyone
    (University of Waterloo, 2018-05-23) El Gebaly, Kareem; Lin, Jimmy; Aboulnaga, Ashraf; Golab, Lukasz
    Analyzing relational data typically involves tasks that facilitate gaining familiarity or insights and coming up with findings or conclusions based on the data. This process is usually practiced by data experts, such as data scientists, who share their output with a potentially less expert audience (everyone). Our goal is to enable everyone to participate in analyzing data rather than passively consuming its outputs (analytics democratization). With today’s increasing availability of data (data democratization) on the internet (web) combined with already widespread personal computing capabilities such a goal is becoming more attainable. With the recent increase of public data, i.e., Open Data, users without a technical background are keener than ever to analyze new data sets that are relevant to wide sectors of society. An important example of Open Data is the data released by governments all over the world, i.e., Open Government. This dissertation focuses on two main challenges that would face data exploration scenarios such as exploring open data found over the web. First, the infrastructure necessary for interactive data exploration is costly and hard to manage, especially by users who do not have technical knowledge. Second, the target users need guidance through the data exploration since there are too many starting points. To eliminate challenges related to managing infrastructure, we propose an in-browser SQL engine (serverless), i.e., a portable database, which we call Afterburner. Afterburner achieves comparable performance to native SQL engines given the same resources on modestly sized data sets. Afterburner uses code generation techniques that target an optimization-amenable subset of JavaScript and employs typed arrays for its columnar-based in-memory storage. In addition, for databases that are too large for the browser, we propose a hybrid architecture to accelerate the performance of data exploration tasks: a one-time SQL query that runs at the backend and SQL queries running in the browser as per user’s interactions. Based on a simple hint by the user, Afterburner automatically splits queries into two parts: a backend query that generates a materialized view that is shipped to the browser, and a frontend query per subsequent interaction occur locally against this view. Optimizing queries using local materialized views inside the browser accelerates query latency without adding any complexity to the backend or the frontend. One common theme among many data exploration tasks revolves around navigating the many different ways to group the data, i.e., exploring the data cube. Thus, to guide the user through data exploration, we apply an information-theoretic technique that picks the most informative parts from the entire data cube of a relational table, which is called Explanation Tables. We evaluate the efficiency and effectiveness of a sampling-based technique for generating explanation tables that achieves comparable quality to an exhaustive technique that considers the entire data cube, with a significant reduction in the run time. In addition, we introduce optimizations to explanation tables to fit the modest resources available in the browser without any external dependencies. In this, we present an SQL engine and a data exploration guidance tool that run entirely in the browser. We view the techniques and the experiments presented here as a fully functional and open-sourced proof of viability of our proposal. Our analytical stack is portable and works entirely in the browser. We show that SQL and exploration guidance can be as accessible as a web page, which opens the opportunity for more people to analyze data sets. Facilitating data exploration for everyone is one step closer towards analytics democratization where everyone can participate in data exploration, not just the experts.
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    Web Data Integration for Non-Expert Users
    (University of Waterloo, 2018-04-26) El-Roby, Ahmed; Aboulnaga, Ashraf
    oday, there is an abundance of structured data available on the web in the form of RDF graphs and relational (i.e., tabular) data. This data comes from heterogeneous sources, and realizing its full value requires integrating these sources so that they can be queried together. Due to the scale and heterogeneity of the data sources on the web, integrating them is typically an automatic process. However, automatic data integration approaches are not completely accurate since they infer semantics from syntax in data sources with a high degree of heterogeneity. Therefore, these automatic approaches can be considered as a first step to quickly get reasonable quality data integration output that can be used in issuing queries over the data sources. A second step is refining this output over time while it is being used. Interacting with the data sources through the output of the data integration system and refining this output requires expertise in data management, which limits the scope of this activity to power users and consequently limits the usability of data integration systems. This thesis focuses on helping non-expert users to access heterogeneous data sources through data integration systems, without requiring the users to have prior knowledge of the queried data sources or exposing them to the details of the output of the data integration system. In addition, the users can provide feedback over the answers to their queries, which can then be used to refine and improve the quality of the data integration output. The thesis studies both RDF and relational data. For RDF data, the thesis focuses on helping non-expert users to query heterogeneous RDF data sources, and utilizing their feedback over query answers to improve the quality of the interlinking between these data sources. For relational data, the thesis focuses on improving the quality of the mediated schema for a set of relational data sources and the semantic mappings between these sources based on user feedback over query answers.