Abstract: This paper proposes new definitions of lower and upper approximations, which are basic concepts of the rough set theory. These definitions follow naturally from the concept of ambiguity introduced in this paper. The new definitions are compared to the classical definitions and are shown to be more general, in the sense that they are the only ones which can be used for any type of indiscernibility or similarity relation.

Abstract: Describes a new, logic-based methodology for analyzing observations. The key features of this "logical analysis of data" (LAD) methodology are the discovery of minimal sets of features that are necessary for explaining all observations and the detection of hidden patterns in the data that are capable of distinguishing observations describing "positive" outcome events from "negative" outcome events. Combinations of such patterns are used for developing general classification procedures. An implementation of this methodology is described in this paper, along with the results of numerical experiments demonstrating the classification performance of LAD in comparison with the reported results of other procedures. In the final section, we describe three pilot studies on applications of LAD to oil exploration, psychometric testing and the analysis of developments in the Chinese transitional economy. These pilot studies demonstrate not only the classification power of LAD but also its flexibility and capability to provide solutions to various case-dependent problems.

Abstract: This paper presents an extension of Disjunctive Datalog (DATALOG/sup V,/spl sim//) by integrity constraints. These are of two types: strong, that is, classical integrity constraints and weak, that is, constraints that are satisfied if possible. While strong constraints must be satisfied, weak constraints express desiderata, that is, they may be violated-actually, their semantics tends to minimize the number of violated instances of weak constraints. Weak constraints may be ordered according to their importance to express different priority levels. As a result, the proposed language (call it, DATALOG/sup V,/spl sim/,c/) is well-suited to represent common sense reasoning and knowledge-based problems arising in different areas of computer science such as planning, graph theory optimizations, and abductive reasoning. The formal definition of the language is first given. The declarative semantics of DATALOG/sup V,/spl sim/,c/ is defined in a general way that allows us to put constraints on top of any existing (model-theoretic) semantics for DATALOG/sup V,/spl sim// programs. Knowledge representation issues are then addressed and the complexity of reasoning on DATALOG/sup V,/spl sim/,c/ programs is carefully determined. An in-depth discussion on complexity and expressiveness of DATALOG/sup V,/spl sim/,c/ is finally reported. The discussion contrasts DATALOG/sup V,/spl sim/,c/ to DATALOG/sup V,/spl sim// and highlights the significant increase in knowledge modeling ability carried out by constraints.

Abstract: The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can replace precise and complete probabilistic information, still a useful diagnostic system can be built with imperfect data by introducing domain-dependent constraints. We propose a solution to the problem of determining the combined influences of several diseases on a single test result from specificity and sensitivity data for individual diseases. We also demonstrate two techniques for dealing with unmodeled conditional dependencies in a diagnostic network. These techniques are discussed in the context of an effort to design a portable device for cardiac diagnosis and monitoring from multimodal signals.

Abstract: The authors propose two new parallel formulations of the Apriori algorithm (R Agrawal and R Srikant, 1994) that is used for computing association rules These new formulations, IDD and HD, address the shortcomings of two previously proposed parallel formulations CD and DD Unlike the CD algorithm, the IDD algorithm partitions the candidate set intelligently among processors to efficiently parallelize the step of building the hash tree The IDD algorithm also eliminates the redundant work inherent in DD, and requires substantially smaller communication overhead than DD But IDD suffers from the added cost due to communication of transactions among processors HD is a hybrid algorithm that combines the advantages of CD and DD Experimental results on a 128-processor Cray T3E show that HD scales just as well as the CD algorithm with respect to the number of transactions, and scales as well as IDD with respect to increasing candidate set size

Abstract: This paper examines the inductive inference of a complex grammar with neural networks and specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky (1956), in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagation-through-time training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated.

Abstract: Many semistructured objects are similarly, though not identically structured. We study the problem of discovering "typical" substructures of a collection of semistructured objects. The discovered structures can serve the following purposes: 1) the "table-of-contents" for gaining general information of a source, 2) a road map for browsing and querying information sources, 3) a basis for clustering documents, 4) partial schemas for providing standard database access methods, and 5) user/customer interests and browsing patterns. The discovery task is affected by structural features of semistructured data in a nontrivial way and traditional data mining frameworks are inapplicable. We define this discovery problem and propose a solution.

Abstract: The paper surveys performance models for distributed and replicated database systems. Over the last 20 years (1980-2000), a variety of such performance models have been developed and they differ in: (1) which aspects of a real system are or are not captured in the model (e.g., replication, communication, nonuniform data access, etc.); and (2) how these aspects are modeled. We classify the different alternatives and modeling assumptions and discuss their interdependencies and expressiveness for the representation of distributed databases. This leads to a set of building blocks for analytical performance models. To illustrate the work that is surveyed, we select a combination of these proven modeling concepts and give an example of how to compose a balanced analytical model of a replicated database. We use this example to show how to derive meaningful performance values and to discuss the applicability and expressiveness of performance models for distributed and replicated databases. Finally, we compare the analytical results to measurements in a distributed database system.

Abstract: The construction of a large, complex belief network model, like any major system development effort, requires a structured process to manage system design and development. This paper describes a belief network engineering process based on the spiral system lifecycle model. The problem of specifying numerical probability distributions for random variables in a belief network is best treated not in isolation, but within the broader context of the system development effort as a whole. Because structural assumptions determine which numerical probabilities or parameter values need to be specified, there is an interaction between specification of structure and parameters. Evaluation of successive prototypes serves to refine system requirements, ensure that modeling and elicitation effort are focused productively, and prioritize directions of enhancement and improvement for future prototypes. Explicit representation of semantic information associated with probability assessments facilitates tracing of the rationale for modeling decisions, as well as supporting maintenance and enhancement of the knowledge base.

Abstract: We study similarity queries for time series data where similarity is defined, in a fairly general way, in terms of a distance function and a set of affine transformations on the Fourier series representation of a sequence. We identify a safe set of transformations supporting a wide variety of comparisons and show that this set is rich enough to formulate operations such as moving average and time scaling. We also show that queries expressed using safe transformations can efficiently be computed without prior knowledge of the transformations. We present a query processing algorithm that uses the underlying multidimensional index built over the data set to efficiently answer similarity queries. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We propose a generalization of this algorithm for simultaneously handling multiple transformations at a time, and give experimental results on the performance of the generalized algorithm.

Abstract: Indexing video data is essential for providing content-based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues, even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence, new solutions are significant. This paper develops a data model and a rule-based query language for video content-based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, and (2) video frames which contain these entities.

Abstract: Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on efficient query processing of static data. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. To exploit the locality of the effect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-defined trigger into a set of subtriggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative effect might cause the trigger to fire. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that reevaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well.

Abstract: Functional dependencies (FDs) and inclusion dependencies (INDs) are the most fundamental integrity constraints that arise in practice in relational databases. In this paper, we address the issue of normalization in the presence of FDs and INDs and, in particular, the semantic justification for an inclusion dependency normal form (IDNF), which combines the Boyce-Codd normal form with the restriction on the INDs that they be noncircular and key-based. We motivate and formalize three goals of database design in the presence of FDs and INDs: noninteraction between FDs and INDs, elimination of redundancy and update anomalies, and preservation of entity integrity. We show that (as for FDs), in the presence of INDs, being free of redundancy is equivalent to being free of update anomalies. Then, for each of these properties, we derive equivalent syntactic conditions on the database design. Individually, each of these syntactic conditions is weaker than IDNF and the restriction that an FD is not embedded in the right-hand side of an IND is common to three of the conditions. However, we also show that, for these three goals of database design to be satisfied simultaneously, IDNF is both a necessary and a sufficient condition.

Abstract: Coordination among processes in a distributed system can be rendered very complex in a large-scale system where messages may be delayed or lost and when processes may participate only transiently or behave arbitrarily, e.g. after suffering a security breach. In this paper, we propose a scalable architecture to support coordination in such extreme conditions. Our architecture consists of a collection of persistent data servers that implement simple shared data abstractions for clients, without trusting the clients or even the servers themselves. We show that, by interacting with these untrusted servers, clients can solve distributed consensus, a powerful and fundamental coordination primitive. Our architecture is very practical, and we describe the implementation of its main components in a system called Fleet.

Abstract: Past research in message logging has focused on studying the relative overhead imposed by pessimistic, optimistic and causal protocols during failure-free executions. In this paper, we give the first experimental evaluation of the performance of these protocols during recovery. Our results suggest that applications face a complex tradeoff when choosing a message logging protocol for fault tolerance. On the one hand, optimistic protocols can provide fast failure-free execution and good performance during recovery, but are complex to implement and can create orphan processes. On the other hand, orphan-free protocols either risk being slow during recovery (e.g. sender-based pessimistic and causal protocols) or incur a substantial overhead during failure-free execution (e.g. receiver-based pessimistic protocols). To address this tradeoff, we propose hybrid logging protocols, which are a new class of orphan-free protocols. We show that hybrid protocols perform within 2% of causal logging during failure-free execution and within 2% of receiver-based logging during recovery.

Abstract: Past R-tree studies have focused on the number of nodes visited as a metric of query performance. Since database systems usually include a buffering mechanism, we propose that the number of disk accesses is a more realistic measure of performance. We develop a buffer model to analyze the number of disk accesses required for spatial queries using R-trees. The model can be used to evaluate the quality of R-tree update operations, such as various node splitting and tree restructuring policies, as measured by query performance on the resulting tree. We use our model to study the performance of three well-known R-tree loading algorithms. We show that ignoring buffer behavior and using number of nodes accessed as a performance metric can lead to incorrect conclusions, not only quantitatively, but also qualitatively. In addition, we consider the problem of how many levels of the R-tree should be pinned in the buffer.

Abstract: New applications of information systems need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approach provides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that we prove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our optimizer to utilize more expressive relational rules to provide a wider range of possible optimizations than previous work in SQO. The local optimization algorithm also features a new data structure called AND-OR implication graphs to facilitate the search for optimal queries. These features allow the global optimization to effectively use semantic knowledge to reduce the data transmission cost. We have implemented this approach in the PESTO (Plan Enhancement by SemanTic Optimization) query plan optimizer as a part of the SIMS information mediator. Experimental results demonstrate that PESTO can provide significant savings in query execution cost over query plan execution without optimization.

Abstract: The fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The general configuration of a module is as follows: Major distribution factors define groups of patients, each of them with a definite prevalence of infection caused by a given pathogen. Minor distribution factors multiply the likelihood of one pathogen, without changing much of the prevalence of infection. Infection caused by a pathogen causes local and generalized signs and symptoms. Antibiotic treatment is appropriate if it matches the susceptibility of the pathogens in vitro and appropriate treatment is associated with a gain in life expectancy. This is balanced against the cost of the drug, side effects, and ecological damage, to reach the most cost effective treatment. The net was constructed in such a way that the data for the conditional probability tables will be available, even if it meant sometimes giving up on fine modeling details. For data, we used large databases collected in the last 10 years (1990-2000) and data from the literature. The CPN was a convenient way to combine data from databases collected at different locations and times with published information. Although the net is based on detailed and large databases, its calibration to new sites requires data that is available in most modern hospitals.

Abstract: A ubiquitous information environment can be achieved by the mobile computing technologies. In this environment, users carrying their portable computers can retrieve local or remote information anywhere and at anytime. Data broadcast, with its advantages, has become a powerful means to disseminate data in wireless communications. Indexing methods for the broadcast data have been proposed to speedup access time and reduce power consumption. However, the influence of access failures has not been discussed. For the error-prone mobile environment, the occurrence of access failures is often due to disconnections, handoffs, and communication noises. Based on the distributed indexing scheme, we propose an adaptive access method which tolerates the access failures. The basic idea is to use index replication to recover from the access failures. One mechanism, named search range, is provided to dynamically record the range where the desired data item may exist. According to the search range, an unfinished search can be efficiently resumed by finding an available index replicate. A performance analysis is given to show the benefits of the method. Also, the concept of version bits is applied to deal with the updates of the broadcast data.

Abstract: Proposes a hierarchical error detection framework for a software-implemented fault tolerance (SIFT) layer of a distributed system. A four-level error detection hierarchy is proposed in the context of Chameleon, a software environment for providing adaptive fault tolerance in an environment of commercial off-the-shelf (COTS) system components and software. The design and implementation of a software-based distributed signature monitoring scheme, which is central to the proposed four-level hierarchy, is described. Both intra-level and inter-level optimizations that minimize the overhead of detection and are capable of adapting to runtime requirements are proposed. The paper presents results from a prototype implementation of two levels of the error detection hierarchy and results of a detailed simulation of the overall environment. The results indicate a substantial increase in availability due to the detection framework and help in understanding the tradeoffs between overhead and coverage for different combinations of techniques.

Abstract: An active database is a database in which some operations are automatically executed when specified events happen and particular conditions are met. Several systems supporting active rules in an object oriented data model have been proposed. However, several issues related to the integration of triggers with object oriented modeling concepts have not been satisfactorily addressed. We discuss issues related to trigger inheritance and refinement in the context of the Chimera active object oriented data model. In particular, we introduce a semantics for an active object language that takes into account trigger inheritance and supports trigger overriding. Moreover, we state conditions on trigger overriding ensuring that trigger semantics is preserved in subclasses.

Abstract: In a distributed spatial database system, a user may issue a query that relates two spatial relations not stored at the same site. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computational and transmission costs. In this paper, we address the problems of processing spatial joins in a distributed environment. We propose a semijoin-like operator, called the spatial semijoin, to prune away objects that do not contribute to the join result. This operator also reduces both the transmission and local processing costs for a later join operation. However, the cost of the elimination process must be taken into account, and we consider approaches to minimize these overheads. We also study and compare two families of distributed join algorithms that are based on the spatial semijoin operator. The first is based on multi-dimensional approximations obtained from an index such as the R-tree, and the second is based on single-dimensional approximations obtained from object mapping. We have conducted experiments on real data sets and report the results in this paper.

Abstract: In contemporary computers and networks of computers, various application domains are making increasing demands on the system to move data from one place to another, particularly under some form of soft real-time constraint. A brute force technique for implementing applications in this type of domain demands excessive system resources, even though the actual requirements by different parts of the application vary according to the way it is being used at the moment. A more sophisticated approach is to provide applications with the ability to dynamically adjust resource requirements according to their precise needs, as well as the availability of system resources. This paper describes a set of principles for designing systems to provide support for soft real-time applications using dynamic negotiation. Next, the execution level abstraction is introduced as a specific mechanism for implementing the principles. The utility of the principles and the execution level abstraction is then shown in the design of three resource managers that facilitate dynamic application adaptation: Gryphon, EPA/RT-PCIP, and the DQM architectures.

Abstract: It is striking that the optimization of disjunctive queries-i.e. those which contain at least one OR-connective in the query predicate-has been vastly neglected in the literature, as well as in commercial systems. In this paper, we propose a novel technique, called bypass processing, for evaluating such disjunctive queries. The bypass processing technique is based on new selection and join operators that produce two output streams: the TRUE-stream with tuples satisfying the selection (join) predicate and the FALSE-stream with tuples not satisfying the corresponding predicate. Splitting the tuple streams in this way enables us to "bypass" costly predicates whenever the "fate" of the corresponding tuple (stream) can be determined without evaluating this predicate. In the paper, we show how to systematically generate bypass evaluation plans utilizing a bottom-up building-block approach. We show that our evaluation technique allows us to incorporate the standard SQL semantics of null values. For this, we devise two different approaches: one is based on explicitly incorporating three-valued logic into the evaluation plans; the other one relies on two-valued logic by "moving" all negations to atomic conditions of the selection predicate. We describe how to extend an iterator-based query engine to support bypass evaluation with little extra overhead. This query engine was used to quantitatively evaluate the bypass evaluation plans against the traditional evaluation techniques utilizing a CNFor DNF-based query predicate.

Abstract: This paper describes E-DEVICE, an extensible active knowledge base system (KBS) that supports the processing of event-driven, production, and deductive rules into the same active OODB system. E-DEVICE provides the infrastructure for the smooth integration of various declarative rule types, such as production and deductive rules, into an active OODB system that supports low-level event-driven rules only by: (1) mapping each declarative rule into one event-driven rule, offering centralized rule selection control for correct run-time behavior and conflict resolution, and (2) using complex events to map the conditions of declarative rules and monitor the database to incrementally match those conditions. E-DEVICE provides the infrastructure for easily extending the system by adding: (1) new rule types as subtypes of existing ones, and (2) transparent optimizations to the rule matching network. The resulting system is a flexible, yet efficient, KBS that gives the user the ability to express knowledge in a variety of high-level forms for advanced problem solving in data intensive applications.

Abstract: Real time database systems are expected to rely heavily on indexes to speed up data access and thereby help more transactions meet their deadlines. Accordingly, high performance index concurrency control (ICC) protocols are required to prevent contention for the index from becoming a bottleneck. We develop real time variants of a representative set of classical B-tree ICC protocols and, using a detailed simulation model, compare their performance for real time transactions with firm deadlines. We also present and evaluate a real time ICC protocol called GUARD-link that augments the classical B-link protocol with a feedback based admission control mechanism. Both point and range queries, as well as the undos of the index action transactions are included in the study. The performance metrics used in evaluating the ICC protocols are the percentage of transactions that miss their deadlines and the fairness with respect to transaction type and size. Experimental results show that the performance characteristics of the real time version of an ICC protocol could be significantly different from the performance of the same protocol in a conventional (nonreal time) database system. In particular, B-link protocols, which are reputed to provide the best overall performance in conventional database systems, perform poorly under heavy real time loads. The new GUARD-link protocol, however, although based on the B-link approach, delivers the best performance (with respect to all performance metrics) for a variety of real time transaction workloads, by virtue of its admission control mechanism. GUARD-link provides close to ideal fairness in most environments.

Abstract: Service providers have begun to offer multimedia-on-demand services to residential estates by installing isolated, small-scale multimedia servers at individual estates. Such an arrangement allows the service providers to operate without relying on a highspeed, large-capacity metropolitan area network, which is still not available in many countries. Unfortunately, installing isolated servers can incur very high server costs, as each server requires spare bandwidth to cope with fluctuations in user demand. The authors explore the feasibility of linking up several small multimedia servers to a (limited-capacity) network, and allowing servers with idle retrieval bandwidth to help out servers that are temporarily overloaded; the goal is to minimize the waiting time for service to begin. We identify four characteristics of load sharing in a distributed multimedia system that differentiate it from load balancing in a conventional distributed system. We then introduce a GWQ load sharing algorithm that fits and exploits these characteristics; it puts all servers' pending requests in a global queue, from which a server with idle capacity obtains additional jobs. The performance of the algorithm is captured by an analytical model, which we validate through simulations. Both the analytical and simulation models show that the algorithm vastly reduces wait times at the servers. The analytical model also provides guidelines for capacity planning. Finally, we propose an enhanced GWQ+L algorithm that allows a server to reclaim active local requests that are being serviced remotely. Simulation experiments indicate that the scheduling decisions of GWQ+L are optimal, i.e., it enables the distributed servers to approximate the performance of a large centralized server.

Abstract: Multimedia presentations are composed of objects belonging to different data types such as video, audio, text and image. An important aspect is that, quite often, the user defining a presentation needs to express sophisticated temporal and spatial constraints among the objects composing the presentation. We present a system (called MPGS-Multimedia Presentation Generator System) which supports the specification of constraints among multimedia objects and the generation of multimedia presentations according to the specified constraints. The constraint model provided by MPGS is very flexible and powerful in terms of the kinds of object constraints it can represent. A large number of innovative features are supported including: asynchronous and simultaneous spatial constraints; components of interest and priority levels; motion functions. Obviously, the flexibility provided to the users requires the development of nontrivial techniques to check constraint consistency and to generate a presentation satisfying the specified constraints. We illustrate the solutions we have devised in the framework of MPGS.