2,164 research outputs found
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
Enabling Ontology-based data access to streaming sources
The availability of streaming data sources is progressively increasing thanks to the development of ubiquitous data capturing tech- nologies such as sensor networks. The heterogeneity of these sources in- troduces the requirement of providing data access in a uni
ed and co- herent manner, whilst allowing the user to express their needs at an ontological level. In this paper we describe an ontology-based streaming data access service. Sources link their data content to ontologies through s2o mappings. Users can query the ontology using sparqlStream, an ex- tension of sparql for streaming data. A preliminary implementation of the approach is also presented. With this proposal we expect to set the basis for future e
orts in ontology-based streaming data integration
Sketch of Big Data Real-Time Analytics Model
Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction
Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques
Nearest-neighbor query processing is a fundamental operation for many image
retrieval applications. Often, images are stored and represented by
high-dimensional vectors that are generated by feature-extraction algorithms.
Since tree-based index structures are shown to be ineffective for high
dimensional processing due to the well-known "Curse of Dimensionality",
approximate nearest neighbor techniques are used for faster query processing.
Locality Sensitive Hashing (LSH) is a very popular and efficient approximate
nearest neighbor technique that is known for its sublinear query processing
complexity and theoretical guarantees. Nowadays, with the emergence of
technology, several diverse application domains require real-time
high-dimensional data storing and processing capacity. Existing LSH techniques
are not suitable to handle real-time data and queries. In this paper, we
discuss the challenges and drawbacks of existing LSH techniques for processing
real-time high-dimensional image data. Additionally, through experimental
analysis, we propose improvements for existing state-of-the-art LSH techniques
for efficient processing of high-dimensional image data.Comment: Accepted and Presented at the 5th International Conference on Signal
and Image Processing (SIGI-2019), Dubai, UA
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