World Wide Web (WWW) is the most popular global information sharing and communication system consisting of three standards .i.e., Uniform Resource Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up Language (HTML). Information is provided in text, image, audio and video formats over the web by using HTML which is considered to be unconventional in defining and formalizing the meaning of the context...

Web development is a challenging research area for its creativity and complexity. The existing raised key challenge in web technology technologic development is the presentation of data in machine read and process able format to take advantage in knowledge based information extraction and maintenance [4]. Currently it is not possible to search and extract optimized results using full text queries because there is no such mechanism exists which can fully extract the semantic from full text queries and then look for particular knowledge based information. Mechanism of presenting information over the web in a format so that the humans as well as machines can understand the context leads to the concept of Semantic Web introduced by Tim Berners Lee [4]. Semantic web is a linked mesh of information to produce technologies capable of reasoning on semi structured information and processed by machines [4].

The four intensive problems to the software rose by the software industry .i.e., User System Communication / Human Machine Interface, Meta Data extraction, Information processing & management and Data representation are discussed in this research paper. To contribute in the field we have proposed and described an intelligent semantic oriented agent based search engine including the concepts of intelligent graphical user interface, natural language based information processing, data management and data reconstruction for the final user end information representation.

Ontologies [1] are used in computer science for representing and sharing knowledge about the real world. Usually ontological structures are described in terms of classes(of things) and relationships(between things). This is rather similar to category-theoretic notions of objects and morphisms (see [2, 3] for information about the algebraic category theory). Since the category theory already brings us many benefits in other areasofcomputer science, it is desirable to find arrowtheoretic approaches in the area of knowledge representation. 1 Some authors proposed category-theoretic techniques helpful in different aspects of knowledge representation[5, 6]. Usually they operate with (co)limits that are convenient for merging and interoperating between existing models and metamodels. Our aim is to find a category-theoretic tools that would be useful for description of ontological models from the very beginning.

Magomedov I.A, Khazamov O.A department of Computer Science, Dagestan State Technical University, Makhachkala city, 367014 Abstract The approach of applying associative processor for decision making problem was proposed. It focuses on hardware implementations of fuzzy processing systems, associativity as effective management basis of fuzzy processor. The structural approach is being developed resulting in a quite simple and compact parallel associative memory unit (PAMU). The memory cost and speed comparison of processors with rigid and soft-variable structure is given. Also the example PAMU flashing is considered.

Event-driven automation of reactive functionalities for complex event processing is an urgent need in today's distributed service-oriented architectures and Web-based event-driven environments. An important problem to be addressed is how to correctly and efficiently capture and process the event-based behavioral, reactive logic embodied in reaction rules, and combining this with other conditional decision logic embodied, e.g., in derivation rules. This paper elaborates a homogeneous integration approach that combines derivation rules, reaction rules and other rule types such as integrity constraints into the general framework of logic programming, the industrial-strength version of declarative programming. We describe syntax and semantics of the language, implement a distributed web-based middleware using enterprise service technologies and illustrate its adequacy in terms of expressiveness, efficiency and scalability through examples extracted from industrial use cases. The developed reaction rule language provides expressive features such as modular ID-based updates with support for external imports and self-updates of the intensional and extensional knowledge bases, transactions including integrity testing and roll-backs of update transition paths. It also supports distributed complex event processing, event messaging and event querying via efficient and scalable enterprise middleware technologies and event/action reasoning based on an event/action algebra implemented by an interval-based event calculus variant as a logic inference formalism.

Markov models are extensively used in the analysis of molecular evolution. A recent line of research suggests that pairs of proteins with functional and physical interactions co-evolve with each other. Here, by analyzing hundreds of orthologous sets of three fungi and their co-evolutionary relations, we demonstrate that co-evolutionary assumption may violate the Markov assumption. Our results encourage developing alternative probabilistic models for the cases of extreme co-evolution. Markov models have been extensively used in studies and modeling of molecular evolution (see, for example, [1-11]).

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding theory, we propose a framework for designing sparsity regularization terms which have theoretical and practical advantages when compared to the more standard l0 or l1 ones. The presentation of the framework and theoretical foundations is complemented with examples that show its practical advantages in image denoising, zooming and classification.

The two standard branching schemes for CSPs are d-way and 2-way branching. Although it has been shown that in theory the latter can be exponentially more effective than the former, there is a lack of empirical evidence showing such differences. To investigate this, we initially make an experimental comparison of the two branching schemes over a wide range of benchmarks. Experimental results verify the theoretical gap between d-way and 2-way branching as we move from a simple variable ordering heuristic like dom to more sophisticated ones like dom/ddeg. However, perhaps surprisingly, experiments also show that when state-of-the-art variable ordering heuristics like dom/wdeg are used then d-way can be clearly more efficient than 2-way branching in many cases. Motivated by this observation, we develop two generic heuristics that can be applied at certain points during search to decide whether 2-way branching or a restricted version of 2-way branching, which is close to d-way branching, will be followed. The application of these heuristics results in an adaptive branching scheme. Experiments with instantiations of the two generic heuristics confirm that search with adaptive branching outperforms search with a fixed branching scheme on a wide range of problems.

Because an agent's resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use -- and even create -- opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases(when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations simultaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster (orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.