ViPEr-HiSS: A Case for Storage Design Tools

The viability of large-scale multimedia applications, depends on the performance of storage systems. Providing cost-effective access to vast amounts of video, image, audio, and text data, requires (a) proper configuration of storage hierarchies as well as (b) efficient resource management techniques at all levels of the storage hierarchy. The resulting complexities of the hardware/software co-design in turn contribute to difficulties in making accurate predictions about performance, scalability, and cost-effectiveness of a storage system. Moreover, poor decisions at design time can be costly and problematic to correct in later stages of development. Hence, measurement of systems after they have been developed is not a desirable approach to predicting their performance. What is needed is the ability to evaluate the system's design while there are still opportunities to make corrections to fundamental design flaws. In this paper we describe the framework of ViPEr-HiSS, a tool which facilitates design, development, and subsequent performance evaluation of designs of multimedia storage hierarchies by providing mechanisms for relatively easy experimentation with (a) system configurations as well as (b) application- and media-aware resource management techniques. (Also cross-referenced as UMIACS-TR-99-69

LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur

From software APIs to web service ontologies: a semi-automatic extraction method

Successful employment of semantic web services depends on the availability of high quality ontologies to describe the domains of these services. As always, building such ontologies is difficult and costly, thus hampering web service deployment. Our hypothesis is that since the functionality offered by a web service is reflected by the underlying software, domain ontologies could be built by analyzing the documentation of that software. We verify this hypothesis in the domain of RDF ontology storage tools.We implemented and fine-tuned a semi-automatic method to extract domain ontologies from software documentation. The quality of the extracted ontologies was verified against a high quality hand-built ontology of the same domain. Despite the low linguistic quality of the corpus, our method allows extracting a considerable amount of information for a domain ontology

Analyzing Large Collections of Electronic Text Using OLAP

Computer-assisted reading and analysis of text has various applications in the humanities and social sciences. The increasing size of many electronic text archives has the advantage of a more complete analysis but the disadvantage of taking longer to obtain results. On-Line Analytical Processing is a method used to store and quickly analyze multidimensional data. By storing text analysis information in an OLAP system, a user can obtain solutions to inquiries in a matter of seconds as opposed to minutes, hours, or even days. This analysis is user-driven allowing various users the freedom to pursue their own direction of research