Enabling Graph Analysis Over Relational Databases

Complex interactions and systems can be modeled by analyzing the connections between underlying entities or objects described by a dataset. These relationships form networks (graphs), the analysis of which has been shown to provide tremendous value in areas ranging from retail to many scientific domains. This value is obtained by using various methodologies from network science-- a field which focuses on studying network representations in the real world. In particular "graph algorithms", which iteratively traverse a graph's connections, are often leveraged to gain insights. To take advantage of the opportunity presented by graph algorithms, there have been a variety of specialized graph data management systems, and analysis frameworks, proposed in recent years, which have made significant advances in efficiently storing and analyzing graph-structured data. Most datasets however currently do not reside in these specialized systems but rather in general-purpose relational database management systems (RDBMS). A relational or similarly structured system is typically governed by a schema of varying strictness that implements constraints and is meticulously designed for the specific enterprise. Such structured datasets contain many relationships between the entities therein, that can be seen as latent or "hidden" graphs that exist inherently inside the datasets. However, these relationships can only typically be traversed via conducting expensive JOINs using SQL or similar languages. Thus, in order for users to efficiently traverse these latent graphs to conduct analysis, data needs to be transformed and migrated to specialized systems. This creates barriers that hinder and discourage graph analysis; our vision is to break these barriers. In this dissertation we investigate the opportunities and challenges involved in efficiently leveraging relationships within data stored in structured databases. First, we present GraphGen, a lightweight software layer that is independent from the underlying database, and provides interfaces for graph analysis of data in RDBMSs. GraphGen is the first such system that introduces an intuitive high-level language for specifying graphs of interest, and utilizes in-memory graph representations to tackle the problems associated with analyzing graphs that are hidden inside structured datasets. We show GraphGen can analyze such graphs in orders of magnitude less memory, and often computation time, while eliminating manual Extract-Transform-Load (ETL) effort. Second, we examine how in-memory graph representations of RDBMS data can be used to enhance relational query processing. We present a novel, general framework for executing GROUP BY aggregation over conjunctive queries which avoids materialization of intermediate JOIN results, and wrap this framework inside a multi-way relational operator called Join-Agg. We show that Join-Agg can compute aggregates over a class of relational and graph queries using orders of magnitude less memory and computation time

CubiST++: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees

We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed CubiST++ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, CubiST++ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select and materialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems

Extending APEx (Accuracy-Aware Differentially Private Data Exploration) to Multiple Table Queries

With the recent advances in data analytics and machine learning, organizations are becoming more and more interested in utilizing these techniques to generate insights from the data they have. But the biggest hurdle, especially for those organizations that collect private information, is that it becomes challenging to share their data with data analysts without compromising the privacy of the data. Differential privacy helps to share private data with provable guarantees of privacy for individuals. Even though differential privacy is very good at preserving privacy, it still poses a lot of burden on data analysts to understand differential privacy and its intricate algorithms. Moreover, this also doesn\u27t give any accuracy guarantees to the data analyst. Keeping this in mind, APEx (Accuracy-Aware Differentially Private Data Exploration) was introduced in May 2019, which allows data analysts to run a sequence of queries keeping privacy and accuracy in place. APEx was implemented for only one table in the database. In this research, it is extended and evaluated on multiple table queries

Doctor of Philosophy

dissertationWe are living in an age where data are being generated faster than anyone has previously imagined across a broad application domain, including customer studies, social media, sensor networks, and the sciences, among many others. In some cases, data are generated in massive quantities as terabytes or petabytes. There have been numerous emerging challenges when dealing with massive data, including: (1) the explosion in size of data; (2) data have increasingly more complex structures and rich semantics, such as representing temporal data as a piecewise linear representation; (3) uncertain data are becoming a common occurrence for numerous applications, e.g., scientific measurements or observations such as meteorological measurements; (4) and data are becoming increasingly distributed, e.g., distributed data collected and integrated from distributed locations as well as data stored in a distributed file system within a cluster. Due to the massive nature of modern data, it is oftentimes infeasible for computers to efficiently manage and query them exactly. An attractive alternative is to use data summarization techniques to construct data summaries, where even efficiently constructing data summaries is a challenging task given the enormous size of data. The data summaries we focus on in this thesis include the histogram and ranking operator. Both data summaries enable us to summarize a massive dataset to a more succinct representation which can then be used to make queries orders of magnitude more efficient while still allowing approximation guarantees on query answers. Our study has focused on the critical task of designing efficient algorithms to summarize, query, and manage massive data