1,347 research outputs found
Combining Relational Algebra, SQL, Constraint Modelling, and Local Search
The goal of this paper is to provide a strong integration between constraint
modelling and relational DBMSs. To this end we propose extensions of standard
query languages such as relational algebra and SQL, by adding constraint
modelling capabilities to them. In particular, we propose non-deterministic
extensions of both languages, which are specially suited for combinatorial
problems. Non-determinism is introduced by means of a guessing operator, which
declares a set of relations to have an arbitrary extension. This new operator
results in languages with higher expressive power, able to express all problems
in the complexity class NP. Some syntactical restrictions which make data
complexity polynomial are shown. The effectiveness of both extensions is
demonstrated by means of several examples. The current implementation, written
in Java using local search techniques, is described. To appear in Theory and
Practice of Logic Programming (TPLP)Comment: 30 pages, 5 figure
Open issues in semantic query optimization in relational DBMS
After two decades of research into Semantic Query Optimization (SQO) there is clear agreement as to the efficacy of SQO. However, although there are some experimental implementations there are still no commercial implementations. We
first present a thorough analysis of research into SQO. We identify three problems which inhibit the effective use of SQO in Relational Database Management Systems(RDBMS). We then propose solutions to these problems and describe first steps towards the implementation of an effective semantic query optimizer for relational databases
Big Data Testing Techniques: Taxonomy, Challenges and Future Trends
Big Data is reforming many industrial domains by providing decision support
through analyzing large data volumes. Big Data testing aims to ensure that Big
Data systems run smoothly and error-free while maintaining the performance and
quality of data. However, because of the diversity and complexity of data,
testing Big Data is challenging. Though numerous research efforts deal with Big
Data testing, a comprehensive review to address testing techniques and
challenges of Big Data is not available as yet. Therefore, we have
systematically reviewed the Big Data testing techniques evidence occurring in
the period 2010-2021. This paper discusses testing data processing by
highlighting the techniques used in every processing phase. Furthermore, we
discuss the challenges and future directions. Our findings show that diverse
functional, non-functional and combined (functional and non-functional) testing
techniques have been used to solve specific problems related to Big Data. At
the same time, most of the testing challenges have been faced during the
MapReduce validation phase. In addition, the combinatorial testing technique is
one of the most applied techniques in combination with other techniques (i.e.,
random testing, mutation testing, input space partitioning and equivalence
testing) to find various functional faults through Big Data testing.Comment: 32 page
Constrained set-up of the tGAP structure for progressive vector data transfer
A promising approach to submit a vector map from a server to a mobile client is to send a coarse representation first, which then is incrementally refined. We consider the problem of defining a sequence of such increments for areas of different land-cover classes in a planar partition. In order to submit well-generalised datasets, we propose a method of two stages: First, we create a generalised representation from a detailed dataset, using an optimisation approach that satisfies certain cartographic constraints. Second, we define a sequence of basic merge and simplification operations that transforms the most detailed dataset gradually into the generalised dataset. The obtained sequence of gradual transformations is stored without geometrical redundancy in a structure that builds up on the previously developed tGAP (topological Generalised Area Partitioning) structure. This structure and the algorithm for intermediate levels of detail (LoD) have been implemented in an object-relational database and tested for land-cover data from the official German topographic dataset ATKIS at scale 1:50 000 to the target scale 1:250 000. Results of these tests allow us to conclude that the data at lowest LoD and at intermediate LoDs is well generalised. Applying specialised heuristics the applied optimisation method copes with large datasets; the tGAP structure allows users to efficiently query and retrieve a dataset at a specified LoD. Data are sent progressively from the server to the client: First a coarse representation is sent, which is refined until the requested LoD is reached
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