24,433 research outputs found
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
The SP theory of computing and cognition, described in previous publications,
is an attractive model for intelligent databases because it provides a simple
but versatile format for different kinds of knowledge, it has capabilities in
artificial intelligence, and it can also function like established database
models when that is required.
This paper describes how the SP model can emulate other models used in
database applications and compares the SP model with those other models. The
artificial intelligence capabilities of the SP model are reviewed and its
relationship with other artificial intelligence systems is described. Also
considered are ways in which current prototypes may be translated into an
'industrial strength' working system
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Recommended from our members
Asynchronous data retrieval from an object-oriented database
We present an object-oriented semantic database model which, similar to other object-oriented systems, combines the virtues of four concepts: the functional data model, a property inheritance hierarchy, abstract data types and message-driven computation. The main emphasis is on the last of these four concepts. We describe generic procedures that permit queries to be processed in a purely message-driven manner. A database is represented as a network of nodes and directed arcs, in which each node is a logical processing element, capable of communicating with other nodes by exchanging messages. This eliminates the need for shared memory and for centralized control during query processing. Hence, the model is suitable for implementation on a multiprocessor computer architecture, consisting of large numbers of loosely coupled processing elements
Recommended from our members
Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Implementing PRISMA/DB in an OOPL
PRISMA/DB is implemented in a parallel object-oriented language to gain insight in the usage of parallelism. This environment allows us to experiment with parallelism by simply changing the allocation of objects to the processors of the PRISMA machine. These objects are obtained by a strictly modular design of PRISMA/DB. Communication between the objects is required to cooperatively handle the various tasks, but it limits the potential for parallelism. From this approach, we hope to gain a better understanding of parallelism, which can be used to enhance the performance of PRISMA/DB.\ud
The work reported in this document was conducted as part of the PRISMA project, a joint effort with Philips Research Eindhoven, partially supported by the Dutch "Stimuleringsprojectteam Informaticaonderzoek (SPIN)
A storage and access architecture for efficient query processing in spatial database systems
Due to the high complexity of objects and queries and also due to extremely
large data volumes, geographic database systems impose stringent requirements on their
storage and access architecture with respect to efficient query processing. Performance
improving concepts such as spatial storage and access structures, approximations, object
decompositions and multi-phase query processing have been suggested and analyzed as
single building blocks. In this paper, we describe a storage and access architecture which
is composed from the above building blocks in a modular fashion. Additionally, we incorporate
into our architecture a new ingredient, the scene organization, for efficiently
supporting set-oriented access of large-area region queries. An experimental performance
comparison demonstrates that the concept of scene organization leads to considerable
performance improvements for large-area region queries by a factor of up to 150
Flattening an object algebra to provide performance
Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent result
Rumble: Data Independence for Large Messy Data Sets
This paper introduces Rumble, an engine that executes JSONiq queries on
large, heterogeneous and nested collections of JSON objects, leveraging the
parallel capabilities of Spark so as to provide a high degree of data
independence. The design is based on two key insights: (i) how to map JSONiq
expressions to Spark transformations on RDDs and (ii) how to map JSONiq FLWOR
clauses to Spark SQL on DataFrames. We have developed a working implementation
of these mappings showing that JSONiq can efficiently run on Spark to query
billions of objects into, at least, the TB range. The JSONiq code is concise in
comparison to Spark's host languages while seamlessly supporting the nested,
heterogeneous data sets that Spark SQL does not. The ability to process this
kind of input, commonly found, is paramount for data cleaning and curation. The
experimental analysis indicates that there is no excessive performance loss,
occasionally even a gain, over Spark SQL for structured data, and a performance
gain over PySpark. This demonstrates that a language such as JSONiq is a simple
and viable approach to large-scale querying of denormalized, heterogeneous,
arborescent data sets, in the same way as SQL can be leveraged for structured
data sets. The results also illustrate that Codd's concept of data independence
makes as much sense for heterogeneous, nested data sets as it does on highly
structured tables.Comment: Preprint, 9 page
- …