Scalable Temporal Queries on User-Generated Data

Abstract

With the proliferation of user-generated data, many emerging applications con- sume this data to serve various important domains, such as natural disaster management, citizen journalism, social recommendations, targeted advertising, and scientific research. This data mostly comes in streaming fashion with tremendous high rates and adds up to large archives of historical data. This dissertation studies indexing and querying this data in different contexts in order to support low latency queries. First, we evaluate ten different indexes that support spatial-keyword queries on streaming data at the system level. These queries, namely range query and k-nearest neigh- bors, are extended to include the time dimension in addition to the space and keywords to effectively serve streaming spatial data applications. Supporting such queries on streaming environment is challenging as streaming data comes in a very high rate, and query answer is likely changing around the clock. The extensive evaluation provides insights for the system builders on the potential loss and gain of employing one index over the others from the system perspectives.