The popularity of distributed computing platforms (e.g., Hadoop) is largely due to their ability to address scalability issues that arise from data storage and processing limitations of standard computing systems. However, the decision to dedicate organizational resources and capital for such systems needs a careful consideration of several factors including evaluation of cloud-based distributed computing options. We propose a framework of metrics which we used to conduct an in-depth performance and cost benefit analysis of two standard Hadoop infrastructural choices, i.e., a Platform as a Service (PaaS) on-demand cloud setup and a local organizational setup. We evaluated the framework by means of an exploratory data analysis use-case for a large-scale graph processing research problem. Our analysis considered highly granular aspects of distributed computing performance and studied how utilization rates and infrastructure amortization times affect break-even times. We identified that virtual memory management adversely affects the performance of a cloud cluster during the reduce phase with the magnitude of degradation dependent on the type of MapReduce operation. Our study is intended not only as an evaluation of infrastructural choices but also a development of a metric framework that can serve as a baseline for researchers examining distributed infrastructures.

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the state inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features, and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network-based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.

Provenance is becoming increasingly important as more and more people are using data that they themselves did not generate. In the last decade, significant efforts have been directed toward developing generic, shared data provenance ontologies that support the interoperability of provenance across systems. An issue that is impeding the use of such provenance ontologies is that a generic provenance ontology, no matter how complete it is, is insufficient for capturing the diverse, complex provenance requirements in different domains. In this paper, the authors propose a novel approach to adapting and extending the W7 model, a well-known generic ontology of data provenance. Relying on various knowledge expansion mechanisms provided by the Conceptual Graph formalism, the authors' approach enables us to develop domain ontologies of provenance in a disciplined yet flexible way.

Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.

Time provides context for all our experiences, cognition, and coordinated collective action. Prior research in linguistics, artificial intelligence, and temporal databases suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those are intrinsically devoid of culmination (i.e., atelic). To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual model. Our temporal conceptual design approach involves: 1) capturing "what" semantics using a conventional conceptual model; 2) employing annotations to differentiate between telic and atelic data semantics that help capture "when" semantics; 3) specifying temporal constraints, specifically nonsequenced semantics, in the temporal data dictionary as metadata. Our proposed approach provides a mechanism to represent telic/atelic temporal semantics using temporal annotations. We also show how these semantics can be formally defined using constructs of the conventional conceptual model and axioms in first-order logic. Via what we refer to as the "semantics of composition," i.e., semantics implied by the interaction of annotations, we illustrate the logical consequences of representing telic/atelic data semantics during temporal conceptual design.

Is it possible to effectively spread news articles to a large audience using 140 characters? How does the microblogging website Twitter get used as a platform for the news media agencies to create awareness about the articles they publish on a daily basis? Our study of the diffusion patterns of news articles from 12 popular news sources, including BBC, New York Times, and Mash able on Twitter reveals that a large number of users not only consume and comment on these news articles but also share them in different ways. Combining the methods of network and temporal analyses, we examine and report on how news articles diffuse on Twitter, and how different propagation mechanisms result in different life spans for news articles.

Several methods have been proposed for cascading other classification algorithms with decision tree learners to alleviate the representational bias of decision trees and, potentially, to improve classification accuracy. Such cascade generalization of decision trees increases the flexibility of the decision boundaries between classes and promotes better fitting of the training data. However, more flexible models may not necessarily lead to more predictive power. Because of potential overfitting problems, the true classification accuracy on test data may not increase. Recently, a generic method for cascade generalization has been proposed. The method uses a parameter — the maximum cascading depth — to constrain the degree that other classification algorithms are cascaded with decision tree learners. A method for efficiently learning a collection (i.e., a forest) of generalized decision trees, each with other classification algorithms cascaded to a particular depth, also has been developed. In this article, we propose several new strategies, including elitist and ensemble (weighted or unweighted), for using the various decision trees in such a collection in the prediction phase. Our empirical evaluation using 32 data sets in the UCI machine learning repository shows that, on average, the elitist strategy outperforms the weighted full ensemble strategy, which, in turn, outperforms the unweighted full ensemble strategy. However, no strategy is universally superior across all applications. Since the same training process can be used to evaluate the various strategies, we recommend that several promising strategies be evaluated and compared before selecting the one to use for a given application.

Interschema relationship identification (IRI), that is, determining the relationships among schema elements in heterogeneous data sources, is an important step in integrating the data sources. This article proposes a cluster analysis based approach to semi-automating the IRI process, which is typically very time-consuming and requires extensive human interaction. The authors apply multiple clustering techniques, including K-means, hierarchical clustering, and self-organizing map (SOM) neural network, to identify similar schema elements from heterogeneous data sources, based on a combination of features such as naming similarity, document similarity, schema specification, data patterns, and usage patterns. An SOM prototype the authors have developed provides users with a visualization tool for display of clustering results as well as for incremental evaluation of candidate similar elements.

While many real-world applications need to organize data based on space (e.g., geology, geomarketing, environmental modeling) and/or time (e.g., accounting, inventory management, personnel management), existing conventional conceptual models do not provide a straightforward mechanism to explicitly capture the associated spatial and temporal semantics. As a result, it is left to database designers to discover, design, and implement - on an ad hoc basis - the temporal and spatial concepts that they need. We propose an annotation-based approach that allows a database designer to focus first on nontemporal and nongeospatial aspects (i.e., "what") of the application and, subsequently, augment the conceptual schema with geospatiotemporal annotations (i.e., "when" and "where"). Via annotations, we enable a supplementary level of abstraction that succinctly encapsulates the geospatiotemporal data semantics and naturally extends the semantics of a conventional conceptual model. An overarching assumption in conceptual modeling has always been that expressiveness and formality need to be balanced with simplicity. We posit that our formally defined annotation-based approach is net only expressive, but also straightforward to understand and implement.

Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.

While decision tree techniques have been widely used in classification applications, a shortcoming of many decision tree inducers is that they do not learn intermediate concepts, i.e., at each node, only one of the original features is involved in the branching decision. Combining other classification methods, which learn intermediate concepts, with decision tree inducers can produce more flexible decision boundaries that separate different classes, potentially improving classification accuracy. We propose a generic algorithm for cascade generalization of decision tree inducers with the maximum cascading depth as a parameter to constrain the degree of cascading. Cascading methods proposed in the past, i.e., loose coupling and tight coupling, are strictly special cases of this new algorithm. We have empirically evaluated the proposed algorithm using logistic regression and C4.5 as base inducers on 32 UCI data sets and found that neither loose coupling nor tight coupling is always the best cascading strategy and that the maximum cascading depth in the proposed algorithm can be tuned for better classification accuracy. We have also empirically compared the proposed algorithm and ensemble methods such as bagging and boosting and found that the proposed algorithm performs marginally better than bagging and boosting on the average.

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.

No abstract available.

Recently, there has been increased interest in information sharing among government agencies, with a view toward improving security, reducing costs and offering better quality service to users of government services. In this work, the authors complement earlier work by proposing a comprehensive methodology called IAIS (Inter Agency Information Sharing) that uses XML to facilitate the definition of information that needs to be shared, the storage of such information, the access to this information and finally the maintenance of shared information. The authors compare IAIS with two alternate methodologies to share information among agencies, and analyze the pros and cons of each. They also show how IAIS leverages the recently proposed XML (extensible markup language) standard to allow for inclusion of various groups’ viewpoints when determining what information should be shared and how it should be structured.

Current conceptual workflow models use either informally defined conceptual models or several formally defined conceptual models that capture different aspects of the workflow, e.g., the data, process, and organizational aspects of the workflow. To the best of our knowledge, there are no algorithms that can amalgamate these models to yield a single view of reality. A fragmented conceptual view is useful for systems analysis and documentation. However, it fails to realize the potential of conceptual models to provide a convenient interface to automate the design and management of workflows. First, as a step toward accomplishing this objective, we propose SEAM (State-Entity-Activity-Model), a conceptual workflow model defined in terms of set theory. Second, no attempt has been made, to the best of our knowledge, to incorporate time into a conceptual workflow model. SEAM incorporates the temporal aspect of workflows. Third, we apply SEAM to a real-life organizational unit's workflows. In this work, we show a subset of the workflows modeled for this organization using SEAM. We also demonstrate, via a prototype application, how the SEAM schema can be implemented on a relational database management system. We present the lessons we learned about the advantages obtained for the organization and, for developers who choose to use SEAM, we also present potential pitfalls in using the SEAM methodology to build workflow systems on relational platforms. The information contained in this work is sufficient enough to allow application developers to utilize SEAM as a methodology to analyze, design, and construct workflow applications on current relational database management systems. The definition of SEAM as a context-free grammar, definition of its semantics, and its mapping to relational platforms should be sufficient also, to allow the construction of an automated workflow design and construction tool with SEAM as the user interface.

In the future, automated stock trading (AST) databases will need to support execution of transactions with time constraints similar to that discussed in real-time databases. Transactions in AST databases repeat from a predictable set of transaction classes where the logical read-sets and write-sets of these classes can be established a priori. We propose a novel transaction management mechanism that exploits this a priori knowledge of data requirements to minimize wasted resources and transaction restarts inherent in existing real-time transaction processing mechanisms based on dynamic acquisition of resources. Preanalysis provides a mechanism to incorporate transaction semantics, identify potentially detrimental conflicts, and induce favorable serialization orders before transactions execute. By blocking potentially conflicting transactions, we incorporate an implicit overload management technique. Scheduling decisions can be made efficient to complement serialization orders, thus reducing unnecessary transaction restarts. Our protocol is evaluated using simulation and is shown to outperform existing real-time concurrency control mechanisms under various workload and resource parameters.

Object-oriented databases are becoming increasingly popular in business. Some areas of research interest include formal object-oriented database models, query optimization, analysis and design techniques, and concurrency control. Many of these issues have been well researched as they pertain to the relational model and to centralized databases. Specifically, many studies have concentrated on relational database concurrency control but few have concentrated on object-oriented database concurrency control. Once a concurrency control mechanism is proposed and implemented, the next phase is to analyze its performance using simulation techniques. This paper includes the simulation study for the object-oriented database concurrency control mechanism named O2C2. Contributions of this study include offering a method of simulating concurrency control in an object-oriented database model. This includes extending prior studies that deal only with reading or writing a single data item without implications to the rest of the database. Specifically, the method of simulating concurrency control in an object-oriented database model is applied to the mechanism O2C2. The simulation demonstrates some intuitive, yet important properties about concurrency control in object-oriented databases. For example, as the mix of transactions progresses from operating mostly with instance objects to operating with more class and method definitions, throughput decreases.Request access from your librarian to read this article's full text.

Recent years have seen an increased interest in providing support for collaborative activities among groups of users participating in various information systems design tasks such as, requirements determination and process modeling. However, little attention has been paid to the collaborative conceptual database design process. In this article, we develop a model of the collaborative conceptual schema development process and describe the design and implementation of a graphical multiuser conceptual schema design tool that is based on the model. The system we describe allows a group of users to work collaboratively on the creation of database schemas in synchronous (same-time) mode (either in a face-to-face or distributed setting). Extensive modeling support is provided to assist users in creating semantically correct conceptual schemas. The system also provides users with several graphical facilities such as, a large drawing workspace with the ability to scroll or “jump” to any portion of this workspace, zooming capabilities, and the ability to move object(s) to any portion of the workspace. The unique component of the system, however, is its built-in support for collaborative schema design. The system supports a relaxed WYSIWIS environment, i.e., each user can control the graphical layout of the same set of schema objects. The system ensures that changes/additions made by any user are consistent. Any conflicts that may compromise to the integrity of the shared schema are flagged and resolved by the system. The results from a preliminary experiment suggest that the use of our system in a collaborative mode improved information sharing among users, minimized conflicts, and led to a more comprehensive schema definition.

The authors' research goal is to develop and validate an expert system that screens innovations prior to commercialization. This is an important research issue because business corporations are highly dependent on innovations for their growth and profitability, yet most corporations suffer from a high rate of new product failure. Few of the existing decision support systems have alleviated this problem, partly because of their inability to deal with nonmathematical (logical) relationships. An expert system for new product planning could save organizations tremendous amounts of resources (such as dollars, time and scientific talent) spent on product failures. The design of the proposed knowledge-based system is built upon the authors' earlier work in this area. The authors have addressed several critical research issues in the development of such a system: choice of the appropriate sources of knowledge, resolution of conflict among human experts chosen for knowledge acquisition, use of knowledge programming techniques that can accommodate uncertainty, and multiple methods of system validation. The research makes several contributions to marketing theory and practice. Most notably, the development of such systems contributes to effective product planning in organizations and enhances resource efficiency. Further, it generates guidelines for capturing and using expertise in highly unstructured decision-making situations such as product management.

Database design is a complex and time-consuming process. In order to automate database design, an understanding of the nature of expertise that goes into the design process is needed. Although a number of expert systems have been developed to assist or replace a database designer, database design expertise has not been examined in any detail. This paper proposes a conceptual framework for explaining this type of expertise. The components of the framework are applied to each phase of the design process and used to provide guidelines for the level of expertise developers might strive to obtain. Several representative systems are analyzed, based on the framework, to explore the degree to which expertise is being captured. Implications for the future development of database design expert systems are discussed.

The problem of database allocation incorporating a specific concurrency control mechanism and queuing delays is addressed in this paper. A mathematical model is presented to aid designers in the development of distributed database systems. The model takes into account the pattern of usage of the databases, communication costs in the network, costs for maintaining consistency among the various copies of a database, and storage costs. A solution procedure based on the principle of Lagrangian relaxation is proposed to solve the model. Some analysis of the model is reported along with several useful observations. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

The impact of incorporating a specific concurrency control mechanism (CCM) into the file allocation problem (FAP) is discussed. Depending on the specific CCM use, the communication flows in a network will vary. To allocate data optimally, one must identify the exact communication flows in the network. It is this aspect that has been ignored in past research on the FAP. A linear mixed-integer programming model formulated for the FAP is given. The model incorporates the WRITE LOCKS ALL-READ LOCKS ONE mechanism for concurrency control. A special algorithm based on the implicit representation of variable upper bounds is developed to solve the model. Detailed analysis for various configurations of a network is performed. Several potential applications for the model are identified.>

A description is given of the architecture and development of a knowledge-based system called MUVIS (multiuser view integration system) to support the design of distributed object-oriented databases. MUVIS is implemented using an object-oriented development environment. It assists database designers in representing user views and integrating these views into a global conceptual view. The view integration component is decoupled from the view modeling component. The underlying data model, the semantic data model, treats all parts of the design as objects, thereby reducing the complexity of the integration.>

The Report Generation Facility is a high-level interface designed and implemented to support coherent access of data stored in a number of stand-alone, independent, database systems served by it. Specifically, the Report Generation Facility provides a menu which allows a user to request one or more standard reports provided by the individual database systems and the Report Generation Facility, or to formulate a query in an English-like query language without having to specify where the requested data reside or how to retrieve them. The Report Generation Facility translates the user's request or query into subtransactions to the individual database systems and submits the substransactions in order to retrieve and format the data requested by the user. Thus, the Report Generation Facility provides a standard way to produce reports based on data stored in the database systems with minimal user effort and expertise. This paper describes its capabilities and architecture.