Path Queries on Compressed XML

Central to any XML query language is a path language such as XPath which operates on the tree structure of the XML document. We demonstrate in this paper that the tree structure can be e#ectively compressed and manipulated using techniques derived from symbolic model checking . Specifically, we show first that succinct representations of document tree structures based on sharing subtrees are highly e#ective. Second, we show that compressed structures can be queried directly and e#ciently through a process of manipulating selections of nodes and partial decompression

Pattern matching of compressed terms and contexts and polynomial rewriting

A generalization of the compressed string pattern match that applies to terms with variables is investigated: Given terms s and t compressed by singleton tree grammars, the task is to find an instance of s that occurs as a subterm in t. We show that this problem is in NP and that the task can be performed in time O(ncjVar(s)j), including the construction of the compressed substitution, and a representation of all occurrences. We show that the special case where s is uncompressed can be performed in polynomial time. As a nice application we show that for an equational deduction of t to t0 by an equality axiom l = r (a rewrite) a single step can be performed in polynomial time in the size of compression of t and l; r if the number of variables is fixed in l. We also show that n rewriting steps can be performed in polynomial time, if the equational axioms are compressed and assumed to be constant for the rewriting sequence. Another potential application are querying mechanisms on compressed XML-data bases

Compressed materialised views of semi-structured data

Query performance issues over semi-structured data have led to the emergence of materialised XML views as a means of restricting the data structure processed by a query. However preserving the conventional representation of such views remains a significant limiting factor especially in the context of mobile devices where processing power, memory usage and bandwidth are significant factors. To explore the concept of a compressed materialised view, we extend our earlier work on structural XML compression to produce a combination of structural summarisation and data compression techniques. These techniques provide a basis for efficiently dealing with both structural queries and valuebased predicates. We evaluate the effectiveness of such a scheme, presenting results and performance measures that show advantages of using such structures

bdbms -- A Database Management System for Biological Data

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach

Designing a resource-efficient data structure for mobile data systems

Designing data structures for use in mobile devices requires attention on optimising data volumes with associated benefits for data transmission, storage space and battery use. For semi-structured data, tree summarisation techniques can be used to reduce the volume of structured elements while dictionary compression can efficiently deal with value-based predicates. This project seeks to investigate and evaluate an integration of the two approaches. The key strength of this technique is that both structural and value predicates could be resolved within one graph while further allowing for compression of the resulting data structure. As the current trend is towards the requirement for working with larger semi-structured data sets this work would allow for the utilisation of much larger data sets whilst reducing requirements on bandwidth and minimising the memory necessary both for the storage and querying of the data

Optimizing XML Compression in XQueC

We present our approach to the problem of optimizing compression choices in the context of the XQueC compressed XML database system. In XQueC, data items are aggregated into containers, which are further grouped to be compressed together. This way, XQueC is able to exploit data commonalities and to perform query evaluation in the compressed domain, with the aim of improving both compression and querying performance. However, different compression algorithms have different performance and support different sets of operations in the compressed domain. Therefore, choosing how to group containers and which compression algorithm to apply to each group is a challenging issue. We address this problem through an appropriate cost model and a suitable blend of heuristics which, based on a given query workload, are capable of driving appropriate compression choices