Ranking Large Temporal Data

Ranking temporal data has not been studied until recently, even though ranking is an important operator (being promoted as a firstclass citizen) in database systems. However, only the instant top-k queries on temporal data were studied in, where objects with the k highest scores at a query time instance t are to be retrieved. The instant top-k definition clearly comes with limitations (sensitive to outliers, difficult to choose a meaningful query time t). A more flexible and general ranking operation is to rank objects based on the aggregation of their scores in a query interval, which we dub the aggregate top-k query on temporal data. For example, return the top-10 weather stations having the highest average temperature from 10/01/2010 to 10/07/2010; find the top-20 stocks having the largest total transaction volumes from 02/05/2011 to 02/07/2011. This work presents a comprehensive study to this problem by designing both exact and approximate methods (with approximation quality guarantees). We also provide theoretical analysis on the construction cost, the index size, the update and the query costs of each approach. Extensive experiments on large real datasets clearly demonstrate the efficiency, the effectiveness, and the scalability of our methods compared to the baseline methods.Comment: VLDB201

Durable Queries over Historical Time Series Data

published_or_final_versio

Doctor of Philosophy

dissertationIn the era of big data, many applications generate continuous online data from distributed locations, scattering devices, etc. Examples include data from social media, financial services, and sensor networks, etc. Meanwhile, large volumes of data can be archived or stored offline in distributed locations for further data analysis. Challenges from data uncertainty, large-scale data size, and distributed data sources motivate us to revisit several classic problems for both online and offline data explorations. The problem of continuous threshold monitoring for distributed data is commonly encountered in many real-world applications. We study this problem for distributed probabilistic data. We show how to prune expensive threshold queries using various tail bounds and combine tail-bound techniques with adaptive algorithms for monitoring distributed deterministic data. We also show how to approximate threshold queries based on sampling techniques. Threshold monitoring problems can only tell a monitoring function is above or below a threshold constraint but not how far away from it. This motivates us to study the problem of continuous tracking functions over distributed data. We first investigate the tracking problem on a chain topology. Then we show how to solve tracking problems on a distributed setting using solutions for the chain model. We studied online tracking of the max function on ""broom"" tree and general tree topologies in this work. Finally, we examine building scalable histograms for distributed probabilistic data. We show how to build approximate histograms based on a partition-and-merge principle on a centralized machine. Then, we show how to extend our solutions to distributed and parallel settings to further mitigate scalability bottlenecks and deal with distributed data

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

Doctor of Philosophy

dissertationLinked data are the de-facto standard in publishing and sharing data on the web. To date, we have been inundated with large amounts of ever-increasing linked data in constantly evolving structures. The proliferation of the data and the need to access and harvest knowledge from distributed data sources motivate us to revisit several classic problems in query processing and query optimization. The problem of answering queries over views is commonly encountered in a number of settings, including while enforcing security policies to access linked data, or when integrating data from disparate sources. We approach this problem by efficiently rewriting queries over the views to equivalent queries over the underlying linked data, thus avoiding the costs entailed by view materialization and maintenance. An outstanding problem of query rewriting is the number of rewritten queries is exponential to the size of the query and the views, which motivates us to study problem of multiquery optimization in the context of linked data. Our solutions are declarative and make no assumption for the underlying storage, i.e., being store-independent. Unlike relational and XML data, linked data are schema-less. While tracking the evolution of schema for linked data is hard, keyword search is an ideal tool to perform data integration. Existing works make crippling assumptions for the data and hence fall short in handling massive linked data with tens to hundreds of millions of facts. Our study for keyword search on linked data brought together the classical techniques in the literature and our novel ideas, which leads to much better query efficiency and quality of the results. Linked data also contain rich temporal semantics. To cope with the ever-increasing data, we have investigated how to partition and store large temporal or multiversion linked data for distributed and parallel computation, in an effort to achieve load-balancing to support scalable data analytics for massive linked data

Influence Analysis for Online Social Networks

Ph.DDOCTOR OF PHILOSOPH