UC Irvine

Over the past decade, the proliferation of mobile devices has generated a variety of data at an unprecedented rate. The trend will be further accelerated by the advent of the Internet-of- Things era. Such data include signals, texts, photos, and videos tagged with date, time, and geo coordinates. The data are structured, semi-structured, or unstructured. Data-processing systems that aim to ingest, store, index, and analyze Big Data must deal with such data efficiently. In response, we have developed Apache AsterixDB, a parallel, semi-structured information management platform, that provides the ability to ingest, store, index, query, and analyze mass quantities of data.

The key contributions of this thesis fall in two major parts. First, in order to store and index newly generated data and make them queryable in a timely manner, a record-level transaction model was designed and implemented in AsterixDB based on the read-committed isolation level. Second, due to the importance of efficient query processing for such dynamic geo- tagged data, we implemented five variants of representative, disk-resident spatial indexing methods on top of the Log-Structured Merge-tree-based (LSM) storage layer in AsterixDB and evaluated their pros and cons in light of the dynamic characteristics of geo-tagged Big Data.