V.S. Subrahmanian. Amalgamating knowledge bases. ACM TDS, 19(2):291--331, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....rest of the systems described here is planned. 6 Related Work There has been significant work on general algorithms for query planning, selective materialization, and the optimization of these from the AI perspective, for example TSIMMIS [5] Information Manifold [23] Infosleuth [27] HERMES [1], SIMS [2] etc. and of course on applying agents as the way to embody these algorithms [24, 32, 12, 22] In Biology, compared to the work being done to create the raw data, all the work on how to organize and retrieve it is relatively small. Most of the work in computer science directed to ....

S. Adali and V.S. Subrahmanian. Amalgamating knowledge bases, III: Distributed mediators. International Journal of Intelligent Cooperative Information Systems, 1994.

....two approaches to handle this problem: Paraconsistent formalisms, in which the amalgamated data may remain inconsistent, but the set of conclusions implied by it is not explosive, i.e. not every fact follows from an inconsistent database. Paraconsistent procedures for integrating data (e.g. [14, 41]) are often based on a paraconsistent reasoning process, such as LFI [13] annotated logics [30, 40] or other non classical proof systems [5, 37] Coherent (consistency base) methods, in which the amalgamated data is revised in order to restore consistency (see, e.g. 6, 8, 11, 25, 31] In ....

....that is obtained by running T in the A system together with an i optimizer [respectively, together with a c optimizer] s.t. Insert = Insert . 6 Related works Coherent integration and proper representation of amalgamated data is extensively studied in the literature (see, e.g. [8, 12, 22, 24, 25, 31 34, 38, 41]) Common approaches for dealing with this task are based on techniques of belief revision [31] methods of resolving contradictions by quantitative considerations (such as majority vote [32] or qualitative ones (e.g. de ning priorities on di erent sources of information or preferring certain ....

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V.S.Subrahmanian. Amalgamating knowledge-bases. ACM Trans. on Database Systems 19(2), pp.291-331, 1994.

....on the important issue of how in this representation different knowledge bases KB 1 ; KB l can be combined into a single KB. Main problems studied. The semantical issue of combining knowledge bases, as well as closely related issues, have been studied in the recent literature, see e.g. [2, 1, 18, 41, 23, 36, 39, 7, 33]. We do not intend to discuss the same issue here; rather, we are interested in tools and algorithms at the operational level, which are needed for the implementation of a suitable semantics. In this context, a principal operation is taking the logical intersection of knowledge bases KB 1 ; ....

V. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. on Database Syst., 19(2):291--331, 1994.

....that what is obtained would properly re ect the combination of the distributed data on one hand , and would still be coherent (in terms of consistency) on the other hand. Coherent integration and proper representation of amalgamated data is extensively studied in the literature (see, e.g. [1, 3, 7, 13, 14, 20 23, 26, 29]) Common approaches for dealing with this task are based on techniques of belief revision [20] methods of resolving contradictions by quantitative considerations (such as majority vote [21] or qualitative ones (e.g. de ning priorities on di erent sources of information or preferring certain ....

.... ones (e.g. de ning priorities on di erent sources of information or preferring certain data over another [2, 4, 5] Other approaches are based on rewriting rules for representing the information in a speci c form [14] or use multiple valued semantics (e.g. annotated logic programs [28, 29] and bilattice based formalisms [12, 22] together with non classical refutation procedures [11, 19, 28] that allow to decode within the language itself some meta information such as con dence factors, amount of belief for against a speci c assertion, etc. Each one of the techniques mentioned ....

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V.S.Subrahmanian. Amalgamating knowledge-bases. ACM Trans. on Database Systems 19(2), 291-331, 1994.

....a subset of the global knowledge. Some formalisms have been proposed for the representation and integration of distributed knowledge and reasoning systems. The most important are based on Multi Modal Logics [9] 1 Labelled Deductive Systems (LDS) and LDS for Modal Logics [6, 1] Annotated Logics [17] and Cooperative Information Systems [4] The underlying idea of these formalisms is that the truth values of certain formulae in different knowledge subsystems are related one another. In modal logics axioms relating different modal operators and constraints between different accessibility ....

....a point of view which is external (above, meta) of both i and j. The definition of a domain relation among languages allows us great flexibility in specifying the different kinds of relations between domains associated to different languages. To clar 1 Similar notations have been introduced by [10, 17, 7, 14] 5 ify things let us consider the following example. Suppose that two observers, Chiara and Luciano, represent a piece of world at two different approximation levels, namely suppose that Chiara describes the people living in a village called Poggio Rusco, whereas Luciano describes the habit of ....

V.S. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. Database Syst., 19(2):291--331, 1994.

....and processing [5] and transparency [7] The definition of a formal semantics for federated databases able to cope with these problems is a key point to understand, specify, and verify the behavior of a federated database. Several approaches have been proposed in the past. An incomplete list is [6, 13, 15]. However they all fail to represent all these issues in a uniform way. This failure is due, from our perspective, to the fact that these approaches are based on a complete description of the world, and the semantics of the databases are built by filtering the information of such a description. ....

....express partiality. Totality affects directionality. Indeed in CIS every interschema constraints from S 1 to S 2 entails the converse interschema assertion in the opposite direction. This prevents CIS to completely represent directionality in the communication between databases. Subrahmanian [15] uses annotated logic [3] to integrate a set of deductive databases in an unique amalgamated database called amalgam. The amalgam, in addition to the disjoint union of the databases, contains a supervisory database. The supervisory database is a set of clauses (called amalgamated clauses) which ....

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V.S. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. Database Syst., 19(2):291--331, 1994.

....of both agents, and therefore only if the databases of the agents are redundant. Distribution, partiality, autonomy, and redundancy generate many problems in integrating the information contained in different databases. Several approaches have been proposed in the past. An incomplete list is [4, 13, 14, 11]. However they all fail to represent all these issues in a uniform way. The goal of this paper is to provide a formal semantics for information integration able to cope with these problems. This is a key point to understand, specify, and verify the behavior of a multi agent system for electronic ....

....express partiality. Totality affects directionality. Indeed in CIS every interschema constraints from S 1 to S 2 entails the converse interschema assertion in the opposite direction. This prevents CIS to completely represent directionality in the communication between databases. Subrahmanian [14] uses annotated logic [2] to integrate a set of deductive databases in an unique amalgamated database called amalgam. The amalgam, in addition to the disjoint union of the databases, contains a supervisory database. The supervisory database is a set of clauses (called amalgamated clauses) which ....

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V.S. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. Database Syst., 19(2):291--331, 1994.

....similar to closed (without free variables) deductive bridge rules. Unlike TSIMMIS, our approach does not provide xed rules for query propagation, query interpretation, result propagation and result interpretation, and enables to represent more exible communication protocols among agents. HERMES [ 2 ] provides a general, declarative language for de ning a mediator. The theory of this language is based on annotated logics [ 3 ] In HERMES a set of knowledge bases (modelled as deductive databases) may be integrated in a unique amalgamated database, called amalgam. On the one hand, HERMES takes ....

Sibel Adali and V.S. Subrahmanian. Amalgamating Knowledge Bases, III: Algorithms, Data Structures, and Query Processing. Journal of Logic Programming, 28(1):45-88, 1996.

....supported by all wrapper agents in order to support query answering. A rst implementation of these tasks, which is correct and complete w.r.t. the formal model, is also presented. 1 Introduction Several reference architectures for information integration have been proposed in the literature [8, 6, 2]. In most of them the key role is played by a mediator, that provides an homogeneous and global model of the di erent information sources, and enables global applications to access the di erent information sources in a transparent way. Mediators suitably distribute queries to the relevant ....

....the di erent sources are similar to closed (without free variables) deductive bridge rules. Unlike TSIMMIS, our approach does not provide xed rules for query (result) propagation and query (result) interpretation, and enables to represent more exible communication protocols among agents. HERMES [2] provides a declarative language for de ning mediators. In HERMES a set of knowledge bases (modelled as deductive databases) may be integrated in a unique amalgamated database, called amalgam. On the one hand, HERMES takes a more general approach than ourselves. Indeed the algorithm is aimed to ....

S. Adali and V.S. Subrahmanian. Amalgamating knowledge bases, III: algorithms, data structures, and query processing. Journal of Logic Programming, 28(1), 1996.

....rest of the systems described here is planned. 6 Related Work There has been significant work on general algorithms for query planning, selective materialization, and the optimization of these from the AI perspective, for example TSIMMIS [5] Information Manifold [20] Infosleuth [23] HERMES [1], SIMS [2] Fig. 6. Overview of gene expression processing organization etc. and of course on applying agents as the way to embody these algorithms [21, 26, 11, 19] In Biology, compared to the work being done to create the raw data, all the work on how to organize and retrieve it is relatively ....

S. Adali and V.S. Subrahmanian. Amalgamating knowledge bases, III: Distributed mediators. International Journal of Intelligent Cooperative Information Systems, 1994.

....above all, probability and membership functions for combining and propagating probability measures through aggregations are needed, complex ontology hierarchies with many degrees of similarity between concepts should be handled. 4. 5 HERMES The Heterogeneous Reasoning and Mediator System (HERMES) [4, 5, 6, 136] is based on a general declarative language for the definition of a mediator [140] which expresses semantic integration of information from diverse data sources and reasoning systems. A facility enables the incremental integration of new systems into an already existing mediator. The system ....

S. Adali and V.S. Subrahmanian. Amalgamating Knowledge Bases, III: Algorithms, Data Structures, and Query Processing. Journal of Logic Programming, 28(1):45--88, 1996.

....on di erent subsets of the global knowledge. Well known formalisms for the representation and integration of distributed knowledge and reasoning systems are based on Multi Modal Logics [7] Labelled Deductive Systems [8] and Labelled Deductive Systems for Modal Logics [4, 2] Annotated Logics [16], and Cooperative Information Systems [3] The underlying Visiting Research Fellow from University of Trento, Italy, supported by the Italian National Research Council (CNR) idea of these formalisms is that the truth values of di erent formulae in di erent subsystems are related one another. ....

....levels. Suppose that the vendor is interested in dealing with lemons, red apples, green apples, and pineapples, whereas the buyer deals with lemons and apples. From the point of view of the buyer, lemons in the domain of the vendor and lemons in its own 1 Similar notations have been introduced by [8, 16, 5, 13]. domain represent the same object of the real world, both red apples and green apples in the domain of the vendor represent the real world object it represents with the object apples, and nally pineapples in the domain of the vendor doesn t correspond to any object in its own domain. This is ....

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V.S. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. Database Syst., 19(2):291-331, 1994.

....data into central warehouses include failures due to single source centralization and keeping the imported data current and consistent. Also, one data structure may not efficiently support all required functions, for example, storing GIS map data in a relational database is very inefficient [2]. 1 This research was supported in part by the US Army Logistics Integration Agency, Alexandria, VA and New Cumberland, PA, USA. Our challenges with respect to visualizing [3, 4] networked [5] heterogeneous data, relationships, and structure are similar to those related to other very large data ....

Adali, S. and V.S. Subrahmanian, Amalgamating Knowledge Bases, III - Algorithms, Data Structures, and Query Processing. Journal of Logic Programming, .

....Inventory 03: The Phillips screwdriver is in the second drawer. In this example, two agents are presenting conflicting information. The likelihood of this event in a widely distributed multi agent system is quite high. Despite research into automating the process of reducing data inconsistency (Adali and Subrahmanian, 1994), such conflict will always exist to some extent. Likely sources of data inconsistency include breakdowns or restrictions in communications, varying periodicity of updates, misinterpretation of soft data (e.g. satellite photos) and variations between local and non local knowledge bases. When a PA ....

S. Adali and V.S. Subrahmanian. 1994. Amalgamating knowledge bases, III: Distributed mediators. International Journal of Intelligent Cooperative Information Systems.

.... filtering programs [62] to agents that monitor the state of the stock market and detect trends in stock prices, to intelligent web search agents [33] to the digital battlefield where agent technology closely monitors and merges information gathered from multiple heterogeneous information sources [4, 56, 57, 94, 103]. In the long run, a platform to support the creation and deployment of multiple software agents will need to inter operate with a wide variety of custom made, as well as legacy software sources. Any definition Def of what it takes for a software package S (in any programming language) to be ....

....DBMSs [26, 72] and relational DBMSs, object oriented DBMSs and or file systems [36, 52, 89] However, to date, the semantics of mediators that take actions has not been explored. The work in this paper builds upon mediation efforts reported upon in our HERMES effort described previously in [20, 61, 61, 94, 63]. The Stanford TSIMMIS project [22] effort aimed at integrating a wide variety of heterogeneous databases, together with a free text indexing system. In contrast, HERMES integrated arbitrary software packages such as an Army Terrain Route Planning System, Jim Hendler s UM Nonlin nonlinear planning ....

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V. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. on Database Syst., 19(2):291--331, 1994.

....agents are built on top of one or more distributed heterogeneous data sources. In order to accomplish this, several systems [1, 2, 8] have been proposed over the years. In this section, we will briefly describe the basic theory behind one such mediated system viz. the HERMES system proposed in [16, 18]. Other approaches to mediation such as [2, 8, 20] may also be adapted to the needs of DIS problems using the principles outlined in the rest of this paper. A domain, D, is an abstraction of databases and software packages and consists of three components: 1) a set Sigma whose elements may be ....

V. Subrahmanian. Amalgamating Knowledge Bases. ACM Trans. on Database Syst., 19(2):291--331, 1994.

....College Park, Maryland 20742. Abstract Integrating knowledge from multiple sources is an important aspect of automated reasoning systems. Wiederhold and his colleagues [5, 6] have proposed the concept of a mediator a device that will express how such an integration is to be achieved. In [1, 2, 11], Subrahmanian et al. presented a uniform declarative and operational framework for mediators for amalgamating multiple knowledge bases and data structures (e.g. relational, object oriented, spatial, and temporal structures) when these knowledge bases (possibly) contain inconsistencies, ....

....from a multiplicity of sources. These sources may represent data and or knowledge about different aspects of a problem in a number of ways. Wiederhold and his colleagues [5, 6] have proposed the concept of a mediator a device that will express how such an integration is to be achieved. In [1, 2, 11], Subrahmanian et al. presented a uniform declarative and operational framework for mediators, based on annotated logics, for amalgamatingmultiple knowledge bases and data structures (e.g. relational, object oriented, spatial, and temporal structures) when these knowledge bases (possibly) contain ....

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S. Adali and V.S. Subrahmanian. Amalgamating knowledge bases, II: Distributed mediators. To appear in International Journal of Intelligent and Cooperative Information Systems(IJICIS), December 1994.

....For instance, certain pictures of Clinton may be better than others. Our system has the capability of finding pictures of the desired quality. The current implementation is part of a much broader implementation effort to integrate multiple heterogeneous databases and multimedia databases [27, 28, 1, 21]. The overall systems architecture is shown in Figure 5. The end user accesses a host of heterogeneous databases, data structures and multimedia databases using the mediator framework. Currently, our system can integrate the following types of data: ffl pictorial data, ffl video data, ffl text ....

....has also been developed. Mediator: The concept of a mediator is due to Gio Wiederhold [30, 31] Intuitively, a mediator is a program that integrates information from multiple databases and or multiple data structures. Till recently, most mediators were implemented as very large C programs. In [27, 21], we have proposed a uniform language to implement mediators. A mediator developer would specify a mediator for a specific application in this language. Mediator Development Environment: In order to assist the mediator develop author, we have provided (and in some cases, are in the process of ....

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V.S. Subrahmanian. (1992) Amalgamating Knowledge Bases, Univ. of Maryland Tech. Report CS-TR-2949, Aug. 1992. Accepted for publication in ACM Transactions on Database Systems.

....For instance, certain pictures of Clinton may be better than others. Our system has the capability of finding pictures of the desired quality. The current implementation is part of a much broader implementation effort to integrate multiple heterogeneous databases and multimedia databases [27, 28, 1, 21]. The overall systems architecture is shown in Figure 5. The end user accesses a host of heterogeneous databases, data structures and multimedia databases using the mediator framework. Currently, our system can integrate the following types of data: ffl pictorial data, ffl video data, ffl text ....

....a spatial arrangement the user feels comfortable with. The theoretical algorithms and definitions provided in this paper are not sterile an implementation based on this theory exists at the University of Maryland. This implementation is built on top of an already existing, successful theory[21, 27, 1] and implementation of mediators for integrating heterogeneous databases and data structures. That implementation has been successfully used for two large scale real world applications by organizations outside the University of Maryland [5, 15] Future work must focus on two issues: conjunctive ....

S. Adali and V.S. Subrahmanian. (1993) Amalgamating Knowledge Bases, III: Distributed Mediators, accepted for publication in: Intl. Journal of Intelligent Cooperative Information Systems.

....need to be executed several times since the same kind of information may be requested over and over by different users. Backtracking is another reason for such a situation. Calling an external program is usually a costly operation because of the memory, CPU requirements and possible network delays [2]. Furthermore, actual packages may levy charges for accessing them. Suppose there is a way to guess some of the answers that will be returned by an external call. If a refutation is found by substituting one of these answers, then there is no need to execute the external domain call. This is ....

S. Adali and V.S. Subrahmanian. (1994) Amalgamating Knowledge Bases, II: Distributed Mediators, accepted for publication in: Intl. Journal of Intelligent Cooperative Information Systems. (ftp://ftp.cs.umd.edu/pub/papers/TRs/3124.ps.Z)

.... and detect trends in stock prices, intelligent web search agents (Etzioni and Weld 1994) digital battlefield agents that monitor and merge information gathered from multiple heterogeneous information sources (Arens, Chee, Hsu, and Knoblock 1993; Labrou and Finin 1994; Labrou and Finin 1997; Subrahmanian 1994; Wiederhold 1993) More recently, we have seen an increase in the number of agents that automatically interact with one another. Such agents can negotiate with each other, participate in auctions, make group consensus decisions, and the like (Kraus 1997; Rosenschein and Zlotkin 1994; Sullivan, ....

Subrahmanian, V. S. (1994). Amalgamating Knowledge Bases. ACM Transactions on Database Systems 19 (2), 291--331.

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V.S. Subrahmanian. Amalgamating knowledge bases. ACM TDS, 19(2):291--331, 1994.

No context found.

V. S. Subrahmanian. Amalgamating knowledge bases. Transaction On Database System, 19(2):291--331, 1994.

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V.S. Subrahmanian, "Amalgamating Knowledge Bases", to be published in ACM Transactions on Database Systems (TODS), 1994.

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Subrahmanian, V. S.: Amalgamating Knowledge Bases. Technical Report, University of Maryland, CS-TR-2949, August 1992.

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