| 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. |
