An attribute-based keyword search for m-Health networks

Journal of Computer Virology and Hacking Techniques - m-Health stands for mobile health, where mobile devices are used for collecting and distributing health-related data. As the information...     

Enhancing knowledge discovery via association-based evolution of neural logic networks

The comprehensibility aspect of rule discovery is of emerging interest in the realm of knowledge discovery in databases. Of the many cognitive and psychological factors relating the comprehensibility of knowledge, we focus on the use of human amenable concepts as a representation language in expressing classification rules. Existing work in neural logic networks (or neulonets) provides impetus for our research; its strength lies in its ability to learn and represent complex human logic in decision-making using symbolic-interpretable net rules. A novel technique is developed for neulonet learning by composing net rules using genetic programming. Coupled with a sequential covering approach for generating a list of neulonets, the straightforward extraction of human-like logic rules from each neulonet provides an alternate perspective to the greater extent of knowledge that can potentially be expressed and discovered, while the entire list of neulonets together constitute an effective classifier. We show how the sequential covering approach is analogous to association-based classification, leading to the development of an association-based neulonet classifier. Empirical study shows that associative classification integrated with the genetic construction of neulonets performs better than general association-based classifiers in terms of higher accuracies and smaller rule sets. This is due to the richness in logic expression inherent in the neulonet learning paradigm.     

A methodology for construct development in MIS research

This paper presents a comprehensive methodology for developing constructs in MIS research. It is applicable to both individual and organizational levels of analysis, depending on the nature of the concept under study. The methodology is presented as a research guide progressing through three stages: (1) domain definition, (2) instrument construction, and (3) evaluation of measurement properties. The methodology addresses six key measurement properties (content validity, factorial validity, reliability, convergent validity, discriminant validity, nomological validity), which are discussed in detail. An assessment of the proposed methodology indicates that its adoption in MIS research would greatly improve the rigor of construct development projects. This is evidenced by the wide range of quality publications that have used its techniques and its performance when compared to a number of prominent standards for assessing construct development research.     

Inter-organizational networks for knowledge sharing and trading

Although companies are increasingly developing complex networks of connections with their partners and customers and shifting their focus towards expanding the knowledge management concept externally, research addressing the management of knowledge across organizational borders is rather sparse. Our aim in the present paper is to develop a typology of cross-organizational networks of information and knowledge flows. In order to arrive at such a typology we examine two issues. The first concerns the locus of control on the processes that enable knowledge flow. The second refers to the tradability of the streams of knowledge that flow among organizational entities. We examine four types of knowledge networks: “knowledge communities”, “knowledge chains”, “knowledge supplies” and “knowledge markets”. For each type of knowledge network, we examine its distinct characteristics, study related examples, consider the associated research challenges and analyze an indicative case.     

Application of neural networks for predicting program faults

Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.     

A framework for knowledge discovery and evolution in databases

A concept for knowledge discovery and evolution in databases is described. The key issues include: using a database query to discover new rules; using not only positive examples (answer to a query), but also negative examples to discover new rules; and harmonizing existing rules with the new rules. A tool for characterizing the exceptions in databases and evolving knowledge as a database evolves is developed     

Neural networks and knowledge engineering

A brief overview is given of the nature of present day artificial neural net computing and the authors emphasize the theme that in this mode of computing, knowledge is not represented symbolically, but in the form of distributed processing and localized decision rules. The authors propose that in neural-net computing, the processing is the representation. In other words, the very nature of the processing encodes the knowledge. There is no place and no need for a separate body of global rules to be used by the network for inferencing. If rules exist at all, they are in the nature of local processing steps carried out at individual processors in response to stimuli from other neurons. The authors develop this theme together with a theme which is closely related to it. The second notion is that neural networks may also be thought of and implemented in terms of heterogeneous networks, rather than always or only in terms of massive arrays of identical elemental processors     

MIS系统查询功能的设计与实现

查询功能是管理信息系统的重要组成部分，是评判管理信息系统性能的重要因素。系统中数据的复杂多样，决定了针对这些数据的查询也应该是多样的。文章根据查询内容分析了查询的种类，并给出了在某管理信息系统中的实现方法。     

Discriminator-based adversarial networks for knowledge graph completion

Neural Computing and Applications - Knowledge graphs (KGs) inherently lack reasoning ability which limits their effectiveness for tasks such as question–answering and query expansion. KG...     

机械厂管理信息系统的分析与设计

通过对机械厂管理信息系统的分析与研究,采用客户机／服务器模式,建立了以网络为中心的计算机环境,利用Rational Rose、C Builder等开发工具,开发出了功能强、运行稳定的机械厂管理信息系统。     

Reconceptualizing knowledge networks for enterprise systems implementation: incorporating domain expertise of knowledge sources and knowledge flow intensity

Research on knowledge acquisition through informal social networks during enterprise system implementation has not accounted for the domain expertise of knowledge sources or the quality of knowledge flows. By using data collected from an enterprise resource planning system implementation, this paper reconceptualizes knowledge networks into subnetworks on the basis of the domain expertise of end users and analyzes knowledge acquisition patterns between subnetworks across workgroups having varying performance outcomes. Expertise-based knowledge patterns and their intensities had significant implications for performance outcomes, reiterating their role in the learning process and emphasizing the need to incorporate them into knowledge networking models.     

领域知识融合与共建研究

为建设国家电网客户服务中心的智能对话系统,需要从大量文档、知识库、对话等数据中提炼知识形成知识图谱,提出一种融合事实图谱和事理图谱的新型知识图谱框架,能够基于多源异构数据共建新型知识图谱,在国网领域精准问答、客服系统知识支撑、对话管理引导、知识推理等方面均有较好性能。融合后的图谱及应用系统在国网客服中心问答平台中投入使用,大幅提升了客服人员工作效率和服务质量。     

Detecting and predicting privacy violations in online social networks

Online social networks have become an essential part of social and work life. They enable users to share, discuss, and create content together with various others. Obviously, not all content is meant to be seen by all. It is extremely important to ensure that content is only shown to those that are approved by the content’s owner so that the owner’s privacy is preserved. Generally, online social networks are promising to preserve privacy through privacy agreements, but still everyday new privacy leakages are taking place. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained. We have developed $\mathcal{PROTOSS}$ , a run time tool for detecting and predicting $\mathcal{PR}\mathrm{ivacy}\ \mathrm{vi}\mathcal{O}\mathrm{la}\mathcal{T}\mathrm{ions}\ \mathrm{in}\ \mathcal{O}\mathrm{nline}\ \mathcal{S}\mathrm{ocial}\ \mathrm{network}\mathcal{S}$ . $\mathcal{PROTOSS}$ captures relations among users, their privacy agreements with an online social network operator, as well as domain-based semantic information and rules. It uses model checking to detect if relations among the users will result in the violation of privacy agreements. It can further use the semantic information to infer possible violations that have not been specified by the user explicitly. In addition to detection, $\mathcal{PROTOSS}$ can predict possible future violations by feeding in a hypothetical future world state. Through a running example, we show that $\mathcal{PROTOSS}$ can detect and predict subtle leakages, similar to the ones reported in real life examples. We study the performance of our system on the scenario as well as on an existing Facebook dataset.     

Spectral evolution in dynamic networks

We introduce and study the spectral evolution model, which characterizes the growth of large networks in terms of the eigenvalue decomposition of their adjacency matrices: In large networks, changes over time result in a change of a graph’s spectrum, leaving the eigenvectors unchanged. We validate this hypothesis for several large social, collaboration, rating, citation, and communication networks. Following these observations, we introduce two link prediction algorithms based on the learning of the changes to a network’s spectrum. These new link prediction methods generalize several common graph kernels that can be expressed as spectral transformations. The first method is based on reducing the link prediction problem to a one-dimensional curve-fitting problem which can be solved efficiently. The second algorithm extrapolates a network’s spectrum to predict links. Both algorithms are evaluated on fifteen network datasets for which edge creation times are known.     

A versatile model for predicting the performance of deflection-routing networks

Deflection routing can be used in networks whose stations have the same number of input and output links. Fixed-length packets arrive synchronously on the station's input links at the beginning output link that offers the shortest path to its destination. Since the number of packet buffers at each output link is finite, the simultaneous contention of two packets for the last buffer of the common output link must be resolved by “deflecting” one of the packets according to a specified criterion (e.g. at random, by destination proximity, or by packet age). Deflection routing can therefore be used with as few as one packet buffer per output link.

The potentially unbounded number of routes that a given packet can take makes analyzing the performance of such networks difficult. Using independence assumptions, we develop an efficient, high-fidelity performance model of deflection routing that allows us to estimated the mean end-to-end packet delay in a network that has any given two-connected topology, a single packet buffer at each output port, and an arbitrary traffic matrix.     

Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

The metabolic syndrome is a set of risk factors that include abdominal obesity, insulin resistance, dyslipidemia and hypertension. It has affected around 25% of adults in the US and become a serious problem in Asian countries recently due to the change in dietary habit and life style. On the other hand, Bayesian networks that are the models to solve the problems of uncertainty provide a robust and transparent formalism for probabilistic modeling, so they have been used as a method for diagnostic or prognostic model in medical domain. Since the K2 algorithm, a well-known algorithm for Bayesian networks structure learning, is influenced by an input order of the attributes, an optimization of BN attribute ordering has been studied as a research issue. This paper proposes a novel ordering optimization method using a genetic algorithm based on medical expert knowledge in order to solve this problem. For experiments, we use the dataset examined twice in 1993 and 1995 in Yonchon County of Korea. It has 18 attributes of 1193 subjects participated in both surveys. Using this dataset, we make the prognostic model of the metabolic syndrome using Bayesian networks with an optimized ordering by evolutionary approach. Through an ordering optimization, the prognostic model of higher performance is constructed, and the optimized Bayesian network model by the proposed method outperforms the conventional BN model as well as neural networks and k-nearest neighbors. Finally, we present the application program using the prognostic model of the metabolic syndrome in order to show the usefulness of the proposed method.     

IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

World Wide Web - Due to the popularity of Spatial Databases, many search engine providers have started to expand their text searching capability to include geographical information. Because of this...     

Causal temporal constraint networks for representing temporal knowledge

In this work we describe causal temporal constraint networks (CTCN) as a new computable model for representing temporal information and efficiently handling causality. The proposed model enables qualitative and quantitative temporal constraints to be established, introduces the representation of causal constraints, and suggests mechanisms for representing inexact temporal knowledge. The temporal handling of information is achieved by structuring the information in different interpretation contexts, linked to each other through an inference mechanism which obtains interpretations that are consistent with the original temporal information. In carrying out inferences, we take into account the temporal relationships between events, the possible inexactitude associated with the events, and the atemporal or static information which affects the interpretation pattern being considered. The proposed schema is illustrated with an application developed using the CommonKADS methodology.     

MIS research: Journal status assessment and analysis

The perceived status of journals in which MIS research is published is an important issue to academics. The number of IS-related journals has increased through the years, yet it is not obvious which are the “leading” journals or what criteria should be used to determine this. This article addresses these issues by: (1) evaluating and analyzing previous studies in which MIS related journals have been ranked; (2) determining top-ranked journals and placing them into two levels, or tiers, based on previous studies; and (3) establishing a profile, through a systematic analysis, of the nine journals in the first tier. This study identified 17 highly ranked journals for publication. Nine were placed in the first tier and eight in the second tier. In addition to IS-specific journals, publications included management science, computer science, and business-oriented journals.     

Forming consensus in the networks of knowledge

Information granules, such as e.g., fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.