Performance of Querying Temporal Attributes in Object-Relational Databases

Abstrac

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

The design and implementation of an infrastructure for multimedia digital libraries

We develop an infrastructure for managing, indexing and serving multimedia content in digital libraries. This infrastructure follows the model of the Web, and thereby is distributed in nature. We discuss the design of the Librarian, the component that manages meta data about the content. The management of meta data has been separated from the media servers that manage the content itself. Also, the extraction of the meta data is largely independent of the Librarian. We introduce our extensible data model and the daemon paradigm that are the core pieces of this architecture. We evaluate our initial implementation using a relational database. We conclude with a discussion of the lessons we learned in building this system, and proposals for improving the flexibility, reliability, and performance of the syste

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

Performance Evaluation of Attribute and Tuple Timestamping In Temporal Relational Database

Modeling temporal database over relational database using 1NF model is considered the most popular approach. This is because of the easy implementation as well as the modeling and querying power of 1NF model. In this paper, we compare a new approach for representing valid-time temporal database (in terms of structure and performance) to the main models in literature with attribute and tuple timestamping. The measurement of the performance is represented by the processing time to get the required temporal data as well as the size of the whole stored temporal data. A test has been performed by running sample queries for the same data in the represented models. Based on the tests, we have found that the new proposed model required less time and used less disk space. Therefore, it is more appropriate for modeling 1NF with interval-based timestamping in relational data model