A dynamic data replication strategy using access-weights in data grids

Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suitable site by data replication strategy can increase the system performance. It shortens the data access time and reduces bandwidth consumption. In this paper, a dynamic data replication mechanism called Latest Access Largest Weight (LALW) is proposed. LALW selects a popular file for replication and calculates a suitable number of copies and grid sites for replication. By associating a different weight to each historical data access record, the importance of each record is differentiated. A more recent data access record has a larger weight. It indicates that the record is more pertinent to the current situation of data access. A Grid simulator, OptorSim, is used to evaluate the performance of this dynamic replication strategy. The simulation results show that LALW successfully increases the effective network usage. It means that the LALW replication strategy can find out a popular file and replicates it to a suitable site without increasing the network burden too much.

The upside of data privacy – delighting customers by implementing data privacy measures

The targeted analysis of customer data becomes increasingly important for data-driven business models. At the same time, the customers’ concerns regarding data privacy have to be addressed properly. Existing research mostly describes data privacy as a necessary evil for compliance and risk management and does not propose specific data privacy measures which address the customers’ concerns. We therefore aim to shed light on the upside of data privacy. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on academic literature, legislative texts, corporate privacy statements, and expert interviews. Next, we leverage the Kano model and data from two internet-based surveys to analyze the measures’ evaluation by customers. From a customer perspective, the implementation of the majority of measures is obligatory as those measures are considered as basic needs of must-be quality. However, delighting measures of attractive quality do exist and have the potential to create a competitive advantage. In this, we find some variation across different industries suggesting that corporations aiming to improve customer satisfaction by superior privacy protection should elicit the demands of their specific target customers.     

Using hyperbolic trees to visualize data generated by patent-centric and group-oriented data processing

<美国专利>6,499,026Rivette, et al.December 24,2002<发明人>     

Training data selection with user’s physical characteristics data for acceleration-based activity modeling

This paper proposes an activity recognition method that models an end user’s activities without using any labeled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user’s physical characteristics such as height and gender to find and select appropriate training data obtained from other users in advance. Then, we model the end user’s activities by using the selected labeled sensor data. Therefore, our method does not require the end user to collect and label her training sensor data. In this paper, we propose and test two methods for finding appropriate training data by using information about the end user’s physical characteristics. Moreover, our recognition method improves the recognition performance without the need for any effort by the end user because the method automatically adapts the activity models to the end user when it recognizes her unlabeled sensor data. We confirmed the effectiveness of our method by using 100 h of sensor data obtained from 40 participants.     

A statistical model for network data analysis: KDD CUP 99’ data evaluation and its comparing with MIT Lincoln Laboratory network data

In network data analysis, research about how accurate the estimation model represents the universe is inevitable. As the speed of the network increases, so will the attacking methods on future generation communication network. To correspond to these wide variety of attacks, intrusion detection systems and intrusion prevention systems also need a wide variety of counter measures. As a result, an effective method to compare and analyze network data is needed. These methods are needed because when a method to compare and analyze network data is effective, the verification of intrusion detection systems and intrusion prevention systems can be trusted.In this paper, we use extractable standard protocol information of network data to compare and analyze the data of MIT Lincoln Lab with the data of KDD CUP 99 (modeled from Lincoln Lab). Correspondence Analysis and statistical analyzing method is used for comparing data.     

Variational Bayesian data analysis on manifold

In this paper, variational inference is studied on manifolds with certain metrics. To solve the problem, the analysis is first proposed for the variational Bayesian on Lie group, and then extended to the manifold that is approximated by Lie groups. Then the convergence of the proposed algorithm with respect to the manifold metric is proved in two iterative processes: variational Bayesian expectation (VB-E) step and variational Bayesian maximum (VB-M) step. Moreover, the effective of different metrics for Bayesian analysis is discussed.     

Fuzzy min–max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.     

A means of CD data Encryption

ForewordsWiththedevelopmentofscience,theimprovementofsoftwareandhardwareofthecomputerisveryrapid.Thisprovidesthespaceofdevelopmentformultimediatechnology.Intheapplicationofpersonaldataandmultimedia,italwaysismadeintoCD-ROMforitsquantitiesofvoiceandimageanditspermanentpreservation.Itisverywelcomeforitsbiggercapability,lowerpriceandeasyschlepped.Withthepopularizationofthemachineofwritingandrecording,moreandmorepeopleuseCD-Rasthemediaofdatastorage.ThereforeCD-ROMhasbeenthemaincarrierofcommu…     

Automated data collection for simulation?

It is argued that the data collection process is the most crucial stage in the model building process. This is primarily due to the influence that data has in providing accurate simulation results. Data collection is an extremely time consuming process predominantly because the task is manually orientated. Hence, automating this process of data collection would be extremely advantageous. This paper presents how simulation could utilise the Corporate Business Systems as the simulation data source. Subsequently a unique interface could be implemented to provide these data directly to the simulation tool. Such an interface would prove to be an invaluable tool for users of simulation.     

Archiving data processing of WinCC V6

在实际中,过程数据是最重要的.项目激活或去激活时,我们都需要对指定格式的数据进行处理.Wincc v6是一款功能强大的HMI系统.它可以组态界面并且可以对数据进行归档.但是由于Wincc v6中的归档数据是会被压缩的,并且不保存在主数据库中,因此我们需要将归档数据导出,以方便进行数据处理.Wincc v6提供VBS的开发环境,用VBS,我们就可以方便得修改数据格式.本文提供了一种解决方案,将归档数据导出到Excel中.     

Fast rendering of massive textured terrain data

This paper proposes a framework based on tile-pyramid model and linear quadtree tile-index, which enables the real-time rendering of out-of-core terrain data sets while guaranteeing geometric and texture accuracy. The Digital Elevation Model pyramid and the orthophoto pyramid are created in advance and the quadtree is used for constructing tile-index and managing data tiles. To achieve real-time loading of terrain dataset, the view frustum culling technology and the target-tiles searching algorithm based on resolution-testing and the tile-request prediction mechanism are used. While rendering the terrain tiles, the dynamic bintree triangulation is used. Experimental results show that based on the current personal computer, this framework can achieve good performance for real-time rendering of massive terrain dataset whose size is unlimited.     

Regimes in baseball players’ career data

In this paper we investigate how we can use gated Bayesian networks, a type of probabilistic graphical model, to represent regimes in baseball players’ career data. We find that baseball players do indeed go through different regimes throughout their career, where each regime can be associated with a certain level of performance. We show that some of the transitions between regimes happen in conjunction with major events in the players’ career, such as being traded or injured, but that some transitions cannot be explained by such events. The resulting model is a tool for managers and coaches that can be used to identify where transitions have occurred, as well as an online monitoring tool to detect which regime the player currently is in.     

A comparison of NOAA–AVHRR fire data with three Landsat data sets in arid and semi-arid Australia

Burnt area data, derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery, are validated in 11 regions of arid and semi-arid Australia, using three separate Landsat-derived burnt area data sets. Mapping accuracy of burnt extent is highly variable between areas and from year to year within the same area. Where there are corresponding patches in the AVHRR and Landsat data sets, the fit is good. However, the AVHRR data set misses some large patches. Overall, 63% of the Landsat burnt area is also mapped in the AVHRR data set, but this varies from 0% to 89% at different sites. In total, 81% of the AVHRR burnt area data are matched in the Landsat data set, but range from 0% to 94%. The lower match rates (<50%) are generally when little area has burnt (0–500 km²), with figures generally better in the more northerly sites. Results of regressions analysis based on 10 km?×?10 km cells are also variable, with R ² values ranging from 0.37 (n?=?116) to 0.94 (n?=?85). For the Tanami Desert scene, R ² varies from 0.41 to 0.61 (n?=?368) over three separate years. Combining the data results in an R ² of 0.60 (n?=?1315) (or 0.56 with the intercept set to 0). The slopes of the regressions indicate that mapping the burnt area from AVHRR imagery underestimates the ‘true’ extent of burning for all scenes and years. Differences in mapping accuracy between low and high fire years are examined, as well as the influence of soil, vegetation, land use and tenure on mapping accuracy. Issues which are relevant to mapping fire in arid and semi-arid environments and discontinuous fuels are highlighted.     

Information fusion approach for the data classification: an example for ERS‐1/2 InSAR data

A huge amount of various remote sensing data have been acquired and archived during recent years. Information extraction from these data is still a challenging task, for example using the data classification. We propose the Bayesian approach to image classification using information fusion from different sources of data. The method of classification is based on the three processing steps: (1) information fission by feature extraction, (2) data and dimensionality reduction by unsupervised clustering, and (3) supervised classification with information fusion. The potential of the classification method is illustrated by the examples on ERS‐1/2 Tandem interferometric synthetic aperture radar data. The continuity of tandem pairs of SAR images is ensured by already started or future missions such as TerraSAR‐X, TanDEM‐X, and COSMO‐SkyMed.     

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.     

Business data mining — a machine learning perspective

The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.     

Attribute dependencies for data with grades I*,†

This paper examines attribute dependencies in data that involve grades, such as a grade to which an object is red or a grade to which two objects are similar. We thus extend the classical agenda by allowing graded, or “fuzzy”, attributes instead of Boolean, yes-or-no attributes in case of attribute implications, and allowing approximate match based on degrees of similarity instead of exact match based on equality in case of functional dependencies. In a sense, we move from bivalence, inherently present in the now-available theories of dependencies, to a more flexible setting that involves grades. Such a shift has far-reaching consequences. We argue that a reasonable theory of dependencies may be developed by making use of mathematical fuzzy logic, a recently developed many-valued logic. Namely, the theory of dependencies is then based on a solid logic calculus the same way classical dependencies are based on classical logic. For instance, rather than handling degrees of similarity in an ad hoc manner, we consistently treat them as truth values, the same way as true (match) and false (mismatch) are treated in classical theories. In addition, several notions intuitively embraced in the presence of grades, such as a degree of validity of a particular dependence or a degree of entailment, naturally emerge and receive a conceptually clean treatment in the presented approach. In the first part of this two-part paper, we discuss motivations, provide basic notions of syntax and semantics and develop basic results which include entailment of dependencies, associated closure structures and a logic of dependencies with two versions of completeness theorem.     

Non-homogeneous dynamic Bayesian networks for continuous data

Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with conditional probabilities in the linear Gaussian family, and a Bayesian multiple changepoint process, where the number and location of the changepoints are sampled from the posterior distribution with MCMC. Our work improves four aspects of an earlier conference paper: it contains a comprehensive and self-contained exposition of the methodology; it discusses the problem of spurious feedback loops in network reconstruction; it contains a comprehensive comparative evaluation of the network reconstruction accuracy on a set of synthetic and real-world benchmark problems, based on a novel discrete changepoint process; and it suggests new and improved MCMC schemes for sampling both the network structures and the changepoint configurations from the posterior distribution. The latter study compares RJMCMC, based on changepoint birth and death moves, with two dynamic programming schemes that were originally devised for Bayesian mixture models. We demonstrate the modifications that have to be made to allow for changing network structures, and the critical impact that the prior distribution on changepoint configurations has on the overall computational complexity.