The increasing prevalence of large graph data has produced a variety of research and applications tailored toward graph data management. Users aiming to perform graph analytics will typically start by importing existing data into a separate graph-purposed storage engine. The cost of maintaining a separate system (e.g., the data copy, the associated queries, etc …) just for graph analytics may be prohibitive for users with Big Data. In this paper, we introduce Graphix and show how it enables property graph views of existing document data in AsterixDB, a Big Data management system boasting a partitioned-parallel query execution engine. We explain a) the graph view user model of Graphix, b)$\text{gSQL}^{++}$, a novel query language extension for synergistic document-based navigational pattern matching, and c) how edge hops are evaluated in a parallel fashion. We then compare queries authored in$\text{gSQL}^{++}$against versions in other leading query languages. Finally, we evaluate our approach against a leading native graph database, Neo4j, and show that Graphix is appropriate for operational and analytical workloads, especially at scale.

Secondary indexes in relational database systems are traditionally built under the assumption that one data record maps to one indexed value. Nowadays, particularly in NoSQL systems, single data records can hold collections of values that users want to access efficiently in an ad-hoc manner. Multi-valued indexes aim to give users the best of both worlds: (i) to keep a more natural data model of records with collections of values, and (ii) to reap the benefits of a secondary index. In this paper, we detail the steps taken to realize multi-valued indexes in AsterixDB, a Big Data management system with a structured query language operating over a collection of documents. This includes (a) creating the specification language for such indexes, (b) illustrating data flows for bulk-loading and maintaining an index, and (c) discussing query plans to take advantage of multi-valued indexes for use in predicates with existential and universal quantification. We conclude with experiments that measure the impact of maintaining an AsterixDB multi-valued index and experiments that compare the query performance our multi-valued indexes against similar indexes in MongoDB and Couchbase Server’s Query Service.

Given a set of query points within an image coordinate system, constellation queries identify the matching points in a database of known points within a standard coordinate system. Constellation queries are an integral part of orientation determination systems used in spacecrafts to orient and navigate themselves. The query points are bright spots in an image captured by a camera on the spacecraft and the database contains known celestial objects in a celestial coordinate system. This paper studies six existing constellation query processing strategies (Angle, Interior Angle, Spherical Triangle, Planar Triangle, Pyramid, Composite Pyramid) using a unified algorithmic framework and presents experimental evaluation of the six strategies. We find that the Pyramid strategy in its simplified form has the best accuracy to runtime ratio given simulated images with false positives, false negatives, and Gaussian noise.