2,130 research outputs found
An XML Query Engine for Network-Bound Data
XML has become the lingua franca for data exchange and integration across administrative and enterprise boundaries. Nearly all data providers are adding XML import or export capabilities, and standard XML Schemas and DTDs are being promoted for all types of data sharing. The ubiquity of XML has removed one of the major obstacles to integrating data from widely disparate sources –- namely, the heterogeneity of data formats.
However, general-purpose integration of data across the wide area also requires a query processor that can query data sources on demand, receive streamed XML data from them, and combine and restructure the data into new XML output -- while providing good performance for both batch-oriented and ad-hoc, interactive queries. This is the goal of the Tukwila data integration system, the first system that focuses on network-bound, dynamic XML data sources. In contrast to previous approaches, which must read, parse, and often store entire XML objects before querying them, Tukwila can return query results even as the data is streaming into the system. Tukwila is built with a new system architecture that extends adaptive query processing and relational-engine techniques into the XML realm, as facilitated by a pair of operators that incrementally evaluate a query’s input path expressions as data is read. In this paper, we describe the Tukwila architecture and its novel aspects, and we experimentally demonstrate that Tukwila provides better overall query performance and faster initial answers than existing systems, and has excellent scalability
Spinning Fast Iterative Data Flows
Parallel dataflow systems are a central part of most analytic pipelines for
big data. The iterative nature of many analysis and machine learning
algorithms, however, is still a challenge for current systems. While certain
types of bulk iterative algorithms are supported by novel dataflow frameworks,
these systems cannot exploit computational dependencies present in many
algorithms, such as graph algorithms. As a result, these algorithms are
inefficiently executed and have led to specialized systems based on other
paradigms, such as message passing or shared memory. We propose a method to
integrate incremental iterations, a form of workset iterations, with parallel
dataflows. After showing how to integrate bulk iterations into a dataflow
system and its optimizer, we present an extension to the programming model for
incremental iterations. The extension alleviates for the lack of mutable state
in dataflows and allows for exploiting the sparse computational dependencies
inherent in many iterative algorithms. The evaluation of a prototypical
implementation shows that those aspects lead to up to two orders of magnitude
speedup in algorithm runtime, when exploited. In our experiments, the improved
dataflow system is highly competitive with specialized systems while
maintaining a transparent and unified dataflow abstraction.Comment: VLDB201
From Nested-Loop to Join Queries in OODB
Most declarative SQL-like query languages for object-oriented database systems are orthogonal languages allowing for arbitrary nesting of expressions in the select-, from-, and where-clause. Expressions in the from-clause may be base tables as well as set-valued attributes. In this paper, we propose a general strategy for the optimization of nested OOSQL queries. As in the relational model, the translation/optimization goal is to move from tuple- to set-oriented query processing. Therefore, OOSQL is translated into the algebraic language ADL, and by means of algebraic rewriting nested queries are transformed into join queries as far as possible. Three different optimization options are described, and a strategy to assign priorities to options is proposed
Ontop: answering SPARQL queries over relational databases
We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases
The ViP2P Platform: XML Views in P2P
The growing volumes of XML data sources on the Web or produced by
enterprises, organizations etc. raise many performance challenges for data
management applications. In this work, we are concerned with the distributed,
peer-to-peer management of large corpora of XML documents, based on distributed
hash table (or DHT, in short) overlay networks. We present ViP2P (standing for
Views in Peer-to-Peer), a distributed platform for sharing XML documents based
on a structured P2P network infrastructure (DHT). At the core of ViP2P stand
distributed materialized XML views, defined by arbitrary XML queries, filled in
with data published anywhere in the network, and exploited to efficiently
answer queries issued by any network peer. ViP2P allows user queries to be
evaluated over XML documents published by peers in two modes. First, a
long-running subscription mode, when a query can be registered in the system
and receive answers incrementally when and if published data matches the query.
Second, queries can also be asked in an ad-hoc, snapshot mode, where results
are required immediately and must be computed based on the results of other
long-running, subscription queries. ViP2P innovates over other similar
DHT-based XML sharing platforms by using a very expressive structured XML query
language. This expressivity leads to a very flexible distribution of XML
content in the ViP2P network, and to efficient snapshot query execution. ViP2P
has been tested in real deployments of hundreds of computers. We present the
platform architecture, its internal algorithms, and demonstrate its efficiency
and scalability through a set of experiments. Our experimental results outgrow
by orders of magnitude similar competitor systems in terms of data volumes,
network size and data dissemination throughput.Comment: RR-7812 (2011
A High Performance XML Querying Architecture
Data exchange on the Internet plays an essential role in electronic business (e-business). A recent trend in e-business is to create distributed databases to facilitate data exchange. In most cases, the distributed databases are developed by integrating existing systems, which may be in different database models, and on different hardware and/or software platforms. Heterogeneity may cause many difficulties. A solution to the difficulties is XML (the Extensible Markup Language). XML is becoming the dominant language for exchanging data on the Internet. To develop XML systems for practical applications, developers have to addresses the performance issues. In this paper, we describe a new XML querying architecture that can be used to build high performance systems. Experiments indicate that the architecture performs better than Oracle XML DB, which is one of the most commonly used commercial DBMSs for XML
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