5,504 research outputs found
Constraint-wish and satisfied-dissatisfied: an overview of two approaches for dealing with bipolar querying
In recent years, there has been an increasing interest in dealing with user preferences in flexible database querying, expressing both positive and negative information in a heterogeneous way. This is what is usually referred to as bipolar database querying. Different frameworks have been introduced to deal with such bipolarity. In this chapter, an overview of two approaches is given. The first approach is based on mandatory and desired requirements. Hereby the complement of a mandatory requirement can be considered as a specification of what is not desired at all. So, mandatory requirements indirectly contribute to negative information (expressing what the user does not want to retrieve), whereas desired requirements can be seen as positive information (expressing what the user prefers to retrieve). The second approach is directly based on positive requirements (expressing what the user wants to retrieve), and negative requirements (expressing what the user does not want to retrieve). Both approaches use pairs of satisfaction degrees as the underlying framework but have different semantics, and thus also different operators for criteria evaluation, ranking, aggregation, etc
A Data Transformation System for Biological Data Sources
Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
Bipolar SQLf: a Flexible Querying Language for Relational Databases
International audienceFlexible querying of information systems allows expressing complex preferences in user queries. Such preferences can be modeled by fuzzy bipolar conditions which are made of constraints c and wishes w and interpreted as "to satisfy c and if possible to satisfy w".We de ne in this article the main elements of the Bipolar SQLf language, which is an SQL-like querying language based on a bipolar relational algebra. This language is an extension of the SQLf language. Basic statements (projection, selection, etc.) are firstly defined in terms of syntax, evaluation and calibration. Then, complex statements, such as bipolar queries based on nesting operators are studied in terms of expression, evaluation, query equivalence and backward compatibility with the SQLf language
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Sets and indices in linear programming modelling and their integration with relational data models
LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases
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