42,662 research outputs found
A Type Language for Calendars
Time and calendars play an important role in databases,
on the Semantic Web, as well as in mobile computing. Temporal data
and calendars require (specific) modeling and processing tools. CaTTS
is a type language for calendar definitions using which one can model
and process temporal and calendric data. CaTTS is based on a "theory
reasoning" approach for efficiency reasons. This article addresses type
checking temporal and calendric data and constraints. A thesis underlying
CaTTS is that types and type checking are as useful and desirable
with calendric data types as with other data types. Types enable
(meaningful) annotation of data. Type checking enhances efficiency and
consistency of programming and modeling languages like database and
Web query languages
An efficient query optimization strategy for spatio-temporal queries in video databases
Cataloged from PDF version of article.The interest for multimedia database management systems has grown rapidly due to the need for the storage of huge volumes of multimedia data in computer systems. An important building block of a multimedia database system is the query processor, and a query optimizer embedded to the query processor is needed to answer user queries efficiently. Query optimization problem has been widely studied for conventional database systems; however it is a new research area for multimedia database systems. Due to the differences in query processing strategies, query optimization techniques used in multimedia database systems are different from those used in traditional databases. In this paper, a query optimization strategy is proposed for processing spatio-temporal queries in video database systems. The proposed strategy includes reordering algorithms to be applied on query execution tree. The performance results obtained by testing the reordering algorithms on different query sets are also presented. (C) 2003 Elsevier Inc. All rights reserved
Query processing in temporal object-oriented databases
This PhD thesis is concerned with historical data management in the context of objectoriented
databases. An extensible approach has been explored to processing temporal object queries within a uniform query framework. By the uniform framework, we mean
temporal queries can be processed within the existing object-oriented framework that is extended from relational framework, by extending the existing query processing
techniques and strategies developed for OODBs and RDBs.
The unified model of OODBs and RDBs in UmSQL/X has been adopted as a basis for this purpose. A temporal object data model is thereby defined by incorporating a time
dimension into this unified model of OODBs and RDBs to form temporal relational-like cubes but with the addition of aggregation and inheritance hierarchies. A query algebra,
that accesses objects through these associations of aggregation, inheritance and timereference, is then defined as a general query model /language. Due to the extensive
features of our data model and reducibility of the algebra, a layered structure of query processor is presented that provides a uniforrn framework for processing temporal object
queries. Within the uniform framework, query transformation is carried out based on a set of transformation rules identified that includes the known relational and object rules plus those pertaining to the time dimension. To evaluate a temporal query involving a path with timereference, a strategy of decomposition is proposed. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one containing the time-stamped class that can be optimized by
making use of the ordering information of temporal data and another an ordinary sub-path (without time-stamped classes) which can be further decomposed and evaluated using
different algorithms. The intermediate results of traversing the two sub-paths are then joined together to create the query output. Algorithms for processing the decomposed query components, i. e., time-related operation algorithms, four join algorithms (nested-loop forward join, sort-merge forward join, nested-loop reverse join and sort-merge reverse join) and their modifications, have been presented with cost analysis and implemented with stream processing techniques using C++. Simulation results are also provided. Both cost analysis and simulation show the effects of time on the query processing algorithms: the join time cost is linearly increased with the expansion in the number of time-epochs (time-dimension in the case of a regular TS). It is also shown that using heuristics that make use of time information can lead to a significant time cost saving. Query processing with incomplete temporal data has also been discussed
A Distributed Path Query Engine for Temporal Property Graphs
Property graphs are a common form of linked data, with path queries used to
traverse and explore them for enterprise transactions and mining. Temporal
property graphs are a recent variant where time is a first-class entity to be
queried over, and their properties and structure vary over time. These are seen
in social, telecom, transit and epidemic networks. However, current graph
databases and query engines have limited support for temporal relations among
graph entities, no support for time-varying entities and/or do not scale on
distributed resources. We address this gap by extending a linear path query
model over property graphs to include intuitive temporal predicates and
aggregation operators over temporal graphs. We design a distributed execution
model for these temporal path queries using the interval-centric computing
model, and develop a novel cost model to select an efficient execution plan
from several. We perform detailed experiments of our Granite distributed query
engine using both static and dynamic temporal property graphs as large as 52M
vertices, 218M edges and 325M properties, and a 1600-query workload, derived
from the LDBC benchmark. We often offer sub-second query latencies on a
commodity cluster, which is 149x-1140x faster compared to industry-leading
Neo4J shared-memory graph database and the JanusGraph / Spark distributed graph
query engine. Granite also completes 100% of the queries for all graphs,
compared to only 32-92% workload completion by the baseline systems. Further,
our cost model selects a query plan that is within 10% of the optimal execution
time in 90% of the cases. Despite the irregular nature of graph processing, we
exhibit a weak-scaling efficiency >= 60% on 8 nodes and >= 40% on 16 nodes, for
most query workloads.Comment: An extended version of the paper that appears in IEEE/ACM
International Symposium on Cluster, Cloud and Internet Computing (CCGrid),
202
STRG-QL: Spatio-Temporal Region Graph Query Language for Video Databases
Copyright 2008 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.In this paper, we present a new graph-based query language and its query processing for a Graph-based Video Database Management System (GVDBMS). Although extensive researches have proposed various query languages for video databases, most of them have the limitation in handling general-purpose video queries. Each method can handle specific data model, query type or application. In order to develop a general-purpose video query language, we first produce Spatio-Temporal Region Graph (STRG) for each video, which represents spatial and temporal information of video objects. An STRG data model is generated from the STRG by exploiting object-oriented model. Based on the STRG data model, we propose a new graph-based query language named STRG-QL, which supports various types of video query. To process the proposed STRG-QL, we introduce a rule-based query optimization that considers the characteristics of video data, i.e., the hierarchical correlations among video segments. The results of our extensive experimental study show that the proposed STRG-QL is promising in terms of accuracy and cost.http://dx.doi.org/10.1117/12.76553
Complex Event Processing (CEP)
Event-driven information systems demand a systematic and automatic processing of events. Complex Event Processing (CEP) encompasses methods, techniques, and tools for processing events while they occur, i.e., in a continuous and timely fashion. CEP derives valuable higher-level knowledge from lower-level events; this knowledge takes the form of so called complex events, that is, situations that can only be recognized as a combination of several events. 1 Application Areas Service Oriented Architecture (SOA), Event-Driven Architecture (EDA), cost-reductions in sensor technology and the monitoring of IT systems due to legal, contractual, or operational concerns have lead to a significantly increased generation of events in computer systems in recent years. This development is accompanied by a demand to manage and process these events in an automatic, systematic, and timely fashion. Important application areas for Complex Event Processing (CEP) are the following
Temporal Stream Algebra
Data stream management systems (DSMS) so far focus on
event queries and hardly consider combined queries to both
data from event streams and from a database. However,
applications like emergency management require combined
data stream and database queries. Further requirements are
the simultaneous use of multiple timestamps after different
time lines and semantics, expressive temporal relations between multiple time-stamps and
exible negation, grouping
and aggregation which can be controlled, i. e. started and
stopped, by events and are not limited to fixed-size time
windows. Current DSMS hardly address these requirements.
This article proposes Temporal Stream Algebra (TSA) so
as to meet the afore mentioned requirements. Temporal
streams are a common abstraction of data streams and data-
base relations; the operators of TSA are generalizations of
the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally.
In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent
blocking expressions on a "syntactical" level
Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project
The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system
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