Dynamic Searchable Symmetric Encryption (DSSE) enables a user to perform encrypted search queries on encrypted data stored on a server. Recently, a notion of Forward Privacy (FP) was introduced to guarantee that a newly added document cannot be linked to previous queries, and to thwart relative attacks and lessen information leakage and its consequences. However, in this paper we show that the forward-private schemes have no advantage (in preventing the related attacks) compared to traditional approaches, and previous attacks are still applicable on FP schemes. In FP approaches, access pattern leakage is still possible and can be employed to uncover the search pattern which can be used by passive and adaptive attacks. To address this issue, we construct a new parallelizable DSSE approach to obfuscate the access and search pattern. Our cost-efficient scheme supports both updates and searches. Our security proof and performance analysis demonstrate the practicality, efficiency, and security of our approach.

A social crowdsourcing community (SCC) is a moderated online community where members participate in activities designed to elicit their opinions concerning topics related to products or services. This emerging paradigm of human computation systems is distinct in which it combines the powers of crowdsourcing and social networking to allow for systematic querying of crowds and synthesizing response data (i.e., contributions) into coherent reports for decision-makers. Since the SCC paradigm is relatively new, we apply methods from social network analysis and related fields to help answer important questions relevant to SCCs. We demonstrate the applicability of these methods by analyzing five years of interactions in a private SCC starting from its inception. This analysis reveals the patterns in members' interactions within the SCC and key factors that affect them. In addition, the analysis reveals the patterns in the evolution of these interactions.

In the above article[1], the byline should have identified the second author with his ORCID (0000-0001-8762-6820). Also, the caption of Fig. 14 should have read as follows:

Multitasking has always been an inherent part of software development and is known as the primary source of interruptions due to task switching in software development teams. Developing software involves a mix of analytical and creative work, and requires a significant load on brain functions, such as working memory and decision making. Thus, task switching in the context of software development imposes a cognitive load that causes software developers to lose focus and concentration while working thereby taking a toll on productivity. To investigate the disruptiveness of task switching and interruptions in software development projects, and to understand the reasons for and perceptions of the disruptiveness of task switching we used a mixed-methods approach including a longitudinal data analysis on 4,910 recorded tasks of 17 professional software developers, and a survey of 132 software developers. We found that, compared to task-specific factors (e.g. priority, level, and temporal stage), contextual factors such as interruption type (e.g. self/external), time of day, and task type and context are a more potent determinant of task switching disruptiveness in software development tasks. Furthermore, while most survey respondents believe external interruptions are more disruptive than self-interruptions, the results of our retrospective analysis reveals otherwise. We found that self-interruptions (i.e. voluntary task switchings) are more disruptive than external interruptions and have a negative effect on the performance of the interrupted tasks. Finally, we use the results of both studies to provide a set of comparative vulnerability and interaction patterns which can be used as a mean to guide decision-making and forecasting the consequences of task switching in software development teams.

In the constantly evolving world of software development, switching back and forth between tasks has become the norm. While task switching often allows developers to perform tasks effectively and may increase creativity via the flexible pathway, there are also consequences to frequent task-switching. For high-momentum tasks like software development, "flow", the highly productive state of concentration, is paramount. Each switch distracts the developers' flow, requiring them to switch mental state and an additional immersion period to get back into the flow. However, the wasted time due to time fragmentation caused by task switching is largely invisible and unnoticed by developers and managers. We conducted a survey with 141 software developers to investigate their perceptions of differences between task switching and task interruption and to explore whether they perceive task switchings as disruptive as interruptions. We found that practitioners perceive considerable similarities between the disruptiveness of task switching (either planned or unplanned) and random interruptions. The high level of cognitive cost and low performance are the main consequences of task switching articulated by our respondents. Our findings broaden the understanding of flow change among software practitioners in terms of the characteristics and categories of disruptive switches as well as the consequences of interruptions caused by daily meetings.

We analyze crowdsourcing communities by creating a detailed process for quantifying individual behaviour in online environments. The key feature of our communities is their social interactions so we call these social crowdsourcing communities (SCC). First, we derive factors based on actions captured about textual contributions. We interpret and name these factors. Then we demonstrate their utility in predicting the quality of team contributions. We capture the actions of members using measurable variables and perform factor analysis on these to produce factors of behaviour in SCCs (i.e. dimensions of behaviour). We derive factor scores for each member. An abstract notion of teams is used that is based on the social interactions. Team scores are then determined by the aggregation of the individual factor scores. The relationship between the team-level factor scores and the quality of contributions made by each team are then used as a proxy for team effectiveness. We found that member behaviour has three dimensions/factors: Impact, Activity, Policing/Rowdiness and there is a linear relationship between a team's contribution quality and their Impact scores. We also found a moderate negative linear relationship between the smallest Activity scores in each team with the quality of their individual contributions. This shows that teams that produce higher quality contributions tend to have higher total and maximum Impact score with lower levels of Activity. Thus, we demonstrate that properly aggregated behavioural factors can predict the quality of team-level contributions.

Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requirements provided by stakeholders. We present ELIC, a generic interactive visual analytics tool to assist analysts during requirements elicitation process. ELICA uses a novel information extraction algorithm based on a combination of Weighted Finite State Transducers (WFSTs) (generative model) and SVMs (discriminative model). ELICA presents the extracted relevant information in an interactive GUI (including zooming, panning, and pinching) that allows analysts to explore which parts of the ongoing conversation (or specification document) match with the extracted information. In this demonstration, we show that ELICA is usable and effective in practice, and is able to extract the related information in real-time. We also demonstrate how carefully designed features in ELICA facilitate the interactive and dynamic process of information extraction.

Due to the interest of organizations and academics, crowdsourcing is emerging as an area of targeted social networking. The recent popularity and notable rise of crowdsourcing provides us with the opportunity to study these emerging communities to standardize and facilitate the crowdsourcing process for future development of such platforms. In this paper, we conduct a large and comprehensive study of the structure of a number of crowdsourcing communities (CCs). We study various properties of association (ASSO) and interaction (INTR) networks in an attempt to compare them with existing networks, such as online social networks (OSNs) and the World Wide Web (WWW) network. We obtained data for five successful CCs with nearly two million vertices and nearly six million edges, as well as data for four popular social network sites, Flickr, YouTube, Orkut, and LiveJournal, with more than 11 million vertices and over 328 million edges. We also obtained WWW data containing over 18 million vertices and over 64 million edges. We believe this is the first structural comparative study of CC networks with social and WWW networks at this scale. Our study reveals that CC networks-both ASSO and INTR- are smaller and less symmetrical than OSNs. Similar to OSNs and WWW, degree distributions of CC networks follow powerlaw distribution. CCs and WWW do not suffer influence dilution as is the case in OSNs. Different than OSNs, members of CC networks tend to connect to others with varying degrees, as is the case with WWW.

In social network applications, a user can disclose their location to other users. For example, a user can post a status update mentioning their location in real time. This feature is known as check-in and the social networks that support this feature are called Geo-Social Networks (GSNs). The popularity of a venue increases as more people check-in. Businesses use these networks to attract more customers. Researchers have studied GSNs to understand why and how people use them. The majority of the work studies how to infer what location a user will visit given the visit time is known. Our approach investigates how to predict the time of a visit (aka - check-in time) given the location is known. Our work studies different GSN features and how they relate to check-in time. We propose an algorithm to generate negative labelled records and adapt binary classification for check-in inference. We evaluate our algorithm using a geo-social dataset and show that our algorithm can predict check-in with high accuracy.

The process of integrating instances of disparate data sources to generate an enterprise-wide data warehouse requires identifying related fact and dimension table attributes. The ability to process privacy-aware user-centric queries across these disparate, but related, multidimensional data items within the framework of a Single Consolidated Data Warehouse (SCDW) poses a much more important challenge. In this paper, we propose a privacy methodology that encapsulates the SCDW and provides privacy protection to the underlying data sources at different user-specifiable levels during query processing. This methodology provides protection from various attack models inherent in its framework architecture. Additionally, the methodology offers efficient privacy-aware query processing on the integrated data warehouse.

The problem of privacy-preserving data mining has been studied extensively in recent years because of its importance as a key enabler in the sharing of massive data sets. Most of the work in privacy has focussed on issues involving the quality of privacy preservation and utility, though there has been little focus on the issue of scalability in privacy preservation. The reason for this is that anonymization has generally been seen as a batch and one-time process in the context of data sharing. However, in recent years, the sizes of data sets have grown tremendously to a point where the effective application of the current algorithms is becoming increasingly difficult. Furthermore, the transient nature of recent data sets has resulted in an increased need for the repeated application of such methods on the newer data sets which have been collected. Repeated application demands even greater computational efficiency in order to be practical. For example, an algorithm with quadratic complexity is unlikely to be implementable in reasonable time over terabyte scale data sets. A bigger issue is that larger data sets are likely to be addressed by distributed frameworks such as MapReduce. In such frameworks, one has to address the additional issue of minimizing data transfer across different nodes, which is the bottleneck. In this paper, we discuss the first approach towards privacy-preserving data mining of very massive data sets using MapReduce. We study two most widely-used privacy models k-anonymity and l-diversity for anonymization, and present experimental results illustrating the effectiveness of the approach.

The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, l-diversity, and t-closeness models. In the presence of inter-attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent identity, attribute, and membership disclosure attack.

Purpose is a key concept in privacy policies. Although some models have been proposed for enforcing purpose-based privacy policies , little has been done in defining formal semantics for purpose, and therefore an effective enforcement mechanism for such policies has remained a challenge. We have developed a framework for expressing and enforcing such policies by giving a formal definition of purpose and proposing a modal-logic language for formally expressing purpose constraints. The semantics of this language are defined over an abstract model of workflows . Based on this formal framework, we discuss some properties of purpose, show how common forms of purpose constraints can be formalized, how purpose-based constraints can be connected to more general access control policies, and how they can be enforced in a workflow-based information system by extending common access control technologies.

Data collection agencies publish sensitive data for legitimate purposes, such as research, marketing and etc. Data publishing has attracted much interest in research community due to the important concerns over the protection of individuals privacy. As a result several sanitization mechanisms with different notions of privacy have been proposed. To be able to measure, set and compare the level of privacy protection, there is a need to translate these different mechanisms to a unified system. In this paper, we propose a novel information theoretic framework for representing a formal model of a mechanism as a noisy channel and evaluating its privacy and utility. We show that deterministic publishing property that is used in most of these mechanisms reduces the privacy guarantees and causes information to leak. The great effect of adversary's background knowledge on this metric is concluded. We also show that using this framework we can compute the sanitization mechanism's preserved utility from the point of view of a data user. By using the specifications of a popular sanitization mechanism, k-anonymity, we analytically provide a representation of this mechanism to be used for its evaluation.

We define a semantic model for purpose, based on which purpose-based privacy policies can be meaningfully expressed and enforced in a business system. The model is based on the intuition that the purpose of an action is determined by its situation among other inter-related actions. Actions and their relationships can be modeled in the form of an action graph which is based on the business processes in a system. Accordingly, a modal logic and the corresponding model checking algorithm are developed for formal expression of purpose-based policies and verifying whether a particular system complies with them. It is also shown through various examples, how various typical purpose-based policies as well as some new policy types can be expressed and checked using our model.

Delegation is a process of sharing access rights by users of an access control model. It facilitates the distribution of authorities in the model. It is also useful in collaborative environments. Despite the advantages, delegation may have an impact on the access control model's security. Allowing users to share access rights without the control of an administrator can be used by malicious users to exploit the model. Delegation may also result in privacy violations if it allows accessing data without the data provider's consent. Even though the consent is taken, the privacy can still be violated if the data is used differently than the data provider agreed. Our work investigates data privacy in delegation. As a contribution, a privacy model is introduced that allows a data provider setting privacy policies that state how their data should be used by different organizations or parties who are interested in their data. Based on this setting, a delegation model is designed to consider the privacy policies in taking delegation decisions and also, to set the data usage criteria for the access right receivers. In addition to privacy policies, several delegation policies and constraint have been used to control delegation operations. Delegation is studied within a party and between two parties.

Since Intrusion Detection Systems (IDSs) operate in real-time, they should be light-weighted to detect intrusions as fast as possible. Distance-based Outlier Detection (DBOD) is one of the most widely-used techniques for detecting outliers due to its simplicity and efficiency. Additionally, DBOD is an unsupervised approach which overcomes the problem of the lack of training datasets with known intrusions. However, since IDSs usually have high-dimensional datasets, using DBOD becomes subject to the curse of the dimensionality problem. Furthermore, intrusion datasets should be normalized before calculating pair-wise distance between observations. The purpose of this research is conduct a comparative study among different normalization methods in conjunction with a well-known feature extraction technique; Principle Component Analysis (PCA). Therefore, the efficiency of these methods as data preprocessing techniques can be investigated when applying DBOD to detect intrusions. Experiments were performed using two kinds of distance metrics; Euclidean distance and Mahalanobis distance. We further examined the PCA using 7 threshold values to indicate the number of Principle components to consider according to their total contribution in the variability of features. These approaches have been evaluated using the KDD Cup 1999 intrusion detection (KDD-Cup) dataset. The main purpose of this study is to find the best attribute normalization method along with the correct threshold value for PCA so that a fast unsupervised IDS can discover intrusions effectively. The results recommended using the Log normalization method combined the Euclidean distance while performing PCA.

Programming-by-example (PbE) as a technique can only make substantial progress by addressing the open problem commonly known as the Semantic Gap. This gap is the discrepancy between what the user intended by their actions, and how the inference engine generalizes the actions for reuse in a program. The Seahawk/Daggoo PbE system attempts to close this gap in a novel way: by using Semantic Web and Web Service resources as the building blocks the user manipulates during the demonstration stage of PbE. This eliminates the need for PbE inference by leveraging domain-specific Web Services and community-based agreements about data meanings. The semantic gap in PbE is closed a priori because of semantic agreement between users and service providers within a domain. The new PbE system has been tested in the domain of Bioinformatics to create Web Service workflows, with justification and results reported here. Fundamentally, Seahawk/Daggoo is domain-agnostics because semantics are defined in Web-based ontologies rather than by the PbE system itself. Novel action-to-workflow concept mappings are also introduced.

This is a preliminary work to extend SQL to support user privacy in social networks. The proposal is to extend the data definition and data manipulation languages to capture privacy-preserved mandatory and discretionary access controls, respectively. Here, we focus on common user privacy requirements, such as purpose, generalization, and retention, used by social networks desiring to support privacy. Hence, each user can discretionarily control the set of privileges over the view representing their profile. We plan to support the extended language with underlying catalogues, algorithms, and prototypes. The objective is to develop a low-cost mechanism to preserve privacy in databases,with applications in social networks, e-health, e-business, e-government, etc.

Twins are two extensions onto SQL to support user privacy in social networks: one is corporate-based and the other is user-centric, corresponding to well-known mandatory access control and discretionary access control models, respectively. This paper illustrates the former. The approach adds some predicates to the CREATE TABLE command to capture common corporate-based user privacy requirements, such as purpose, generalization, and retention, required by social networks desiring to support privacy. Hence, the corporation, when creating the underlying databases, defines what the mandatory privacy policies are with which all users must comply. The extension is supported with underlying catalogues, algorithms, and prototype. The key contribution is a low-cost mechanism to develop new systems-that are privacy-preserved-and to transform legacy database MAC models to their privacy preserving equivalents. Although our examples are from social networks, the results apply to data security and user privacy of other enterprises too.

Privacy is a leading concern for anyone that utilizes computing resources whether shopping on the Internet or visiting their doctor. Legislative acts require enterprises and data collectors to protect the privacy of their customers and data owners. Although privacy policy frameworks such as P3P assist data collectors in demonstrating their privacy policies to customers (i.e. publishing privacy policy on Web sites), insufficient research has been reported to help users visualize privacy policies. This paper presents a privacy policy visualization model based on the predicates of a privacy policy model. The key contribution is to provide a visualization model that facilitates understanding the policies for the data owners and provides the opportunity for the policy officers to better understand the designed policies. Finally, we demonstrate the model with a use case drawn from the policies of an online social network.

Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.

Replication in a data grid reduces access latency and bandwidth consumption. However, when different sites hold replicas of a particular file, there is a significant benefit realized by selecting the best replica from among them. The best replica is the one that optimizes the desired performance criterion such as absolute performance (i.e. speed), cost, security or transfer time. By selecting the best replica, the access latency can be minimized. We develop a predictive framework that uses data from various sources and predicts transfer times of the sites that host replicas. With this estimate, one site can request the replica from the site that has the lowest transfer time. We use a neural network (NN) for transfer time prediction of different sites that currently hold file replicas. We compare the results with a multi-regression model and the simulation results demonstrate that the neural network technique is capable of predicting transfer time more accurately than the regression based model.

Data replication is an excellent technique to move and cache data close to users. By replication, data access performance can be improved dramatically. One of the challenges in data replication is to select the candidate sites where replicas should be placed. We use a multi-objective model to address the replica placement problem. The multi-objective model considers the objectives of p-median and p-center models simultaneously to select the candidate sites that will host replicas. The objective of the p-median model is to find the locations of p possible candidate replication sites by optimizing total (or average) response time; where the p-center model finds p candidate sites by optimizing maximum response time. A grid environment is highly dynamic so user requests and network latency vary constantly. Therefore, candidate sites currently holding replicas may not be the best sites to fetch replica on subsequent requests. We propose a dynamic replica maintenance algorithm that re-allocates to new candidate sites if a performance metric degrades significantly over last K time periods. Simulation results demonstrate that the dynamic maintenance algorithm with multi-objective static placement decision performs best in dynamic environments like data grids.

Relational database systems (RDBSs) are well-known and widely used in many organizations, however, semantic conflicts between the participating RDBSs must be resolved before data can be exchanged between them. Semantic resolution between the RDBSs is extremely difficult to address mainly because participating RDBSs are designed and built independently. Furthermore, individual RDBSs are likely to evolve over time and the changes must be reconciled dynamically. In this paper, we describe an approach to resolve the semantic conflicts between RDBSs automatically while allowing the individual RDBSs to evolve. Relational database ontology (RDBO) is created and used to ensure the semantic descriptions of the individual RDBSs are conformed to a set of vocabularies, structures, and restrictions. We show how a modified reasoning engine is used to validate and infer additional semantic relationships from the existing relationships. We also show how terms defined in different database ontologies are compared to each other semantically using semantic weights and our modified reasoning engine. As a result, RDBSs can intemperate with each other seamlessly and at the correct level of semantics defined in their ontologies.

We propose a static replica placement algorithm that places replicas to sites by optimizing average response time and a dynamic replica maintenance algorithm that re-allocates replicas to new candidate sites if a performance metric degrades significantly over last K time periods. Simulation results demonstrate that the dynamic maintenance algorithm with static placement decisions performs best in dynamic environments like data grids.

Mechanisms to efficiently and effectively transmit up-todate information to clients are of significant interest. Broadcast-based scheduling in hierarchical data dissemination systems are under reported in the literature. In these systems a primary server accepts updates that are broadcast to secondary servers and then to a population of clients upon requests. This paper focuses on data dissemination with update propagation at the primary server side. Our initial study shows that at high update rates, a straightforward broadcast scheduler that ignores clients' access patterns can provide clients with outdated information more than 80% of the time. We propose three broadcast scheduling algorithms that primarily differ in how data dissemination with update propagation is guided at the primary and secondary servers. We present mechanisms based on real and predicted clients' access patterns. We evaluate the new scheduling algorithms by running an extensive set of experiments. The performance study illustrates that the third algorithm, which depends on predictive scheduling at both the primary and the secondary servers, provides the best response time and the reception of up-to-date information.

Many publish/subscribe systems have been built using wireless sensor networks, WSNs, deployed for real-world environmental data collection, security monitoring, and object tracking. However, research efforts on WSN-based publish/subscribe systems have largely focused on routing algorithms leaving data management issues mostly untouched. This paper considers a publish/subscribe system built on top of a sensor network that monitors the occurrences of phenomena. In quest for explanations to the occurrence of a phenomenon, a subscriber poses one-time queries to the sensor network for sensor readings taken seconds or minutes before the reported phenomenon occurred. These types of queries cannot be satisfied by subscriptions since subscriptions are only effective in delivering streams of new phenomena. To efficiently answer such queries, it is imperative that a data farm of sensor readings be cultivated within WSNs. This paper proposes a new algorithm for archiving sensor readings on data farm that leverages the non-volatile memory of sensor nodes in the network. The proposed algorithm takes advantage of the memory space on nodes that have low probabilities of detecting phenomena. By running an extensive set of simulation experiments, the performance results show that the proposed algorithm can provide 32.9% memory gain and 81.8% low communication overhead when compared to an approach in which nodes use only their own physical memory

VIREX is a user-oriented novel approach for visual-querying of relational databases and converting the result into XML. VIREX works even when the catalogue of the relational database is missing; it extracts the required catalogue information by analyzing the database content. Further, VIREX allows the user to define views by specifying on the interactive diagram certain factors to be considered while converting relational data into XML. As a result, VIREX displays on the screen the XML schema that satisfies the specified characteristics and generates colored (easy to read) XML document(s)

Dividing a data set into a training set and a test set is a fundamental component in the preprocessing phase of data mining (DM). Effectively, the choice of the training set is an important factor in deriving good classification rules. Traditional approach for association rules mining divides the dataset into training set and test set based on statistical methods. In this paper, we highlight the weaknesses of the existing approach and hence propose a new methodology that employs genetic algorithm (GA) in the process. In our approach, the original dataset is divided into sample and validation sets. Then, GA is used to find an appropriate split of the sample set into training and test sets. We demonstrate through experiments that using the obtained training set as the input to an association rules mining algorithm generates high accuracy classification rules. The rules are tested on the validation set for accuracy. The results are very satisfactory; they demonstrate the applicability and effectiveness of our approach.

Outlier mining is dedicated to finding data objects, which differ significantly from the rest of the data. Outlier mining has been extensively studied in statistics and recently data mining. However, exploring the Web for outliers has received very little attention in the mining community. Web content outliers are documents with 'varying contents ' compared to similar Web documents taken from the same domain. Mining Web content outliers may lead to the identification of competitors and emerging business patterns in electronic commerce. This paper proposes WCOND-mine algorithm for mining Web content outliers using n-grams without a domain dictionary. Experimental results with embedded motifs show that WCOND-mine is capable of finding Web content outliers from Web datasets.

The University of Calgary introduced a controversial course in the fall of 2003 on computer viruses and malware. The primary objection about this course from the anti-virus community was that students were being taught how to create viruses in addition to defending against them. Unfortunately, the reaction to our course was based on a dearth of information, which we remedy in this paper by describing key pedagogical elements of the course.Specifically, we present four aspects of our course: how students are vetted for entry, operation of the course, course content, and the instructional materials used. In addition, we pay particular attention to the controversial course assignments, discussing the assignments and the need for balance, objectivity, security, and learning in a university environment. Our experiences with the course and future plans may be helpful for other institutions considering such course offerings. It should also provide opponents of the course with valuable information about the true nature of the course, the pedagogy used, and the value provided to the computer community as computer science graduates with this kind of expertise take their place as the next generation computer security experts.

MOTIVATION: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. RESULTS: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided. CONTACT: [email protected].

There are several applications of sequential web mining, which is used to find the frequent subsequences in a web log in the World Wide Web (the web). We implemented a tool to analyze the sequential behavior of web log access patterns in multiple-dimensions. Sequences of frequent access patterns may change temporally and spatially. Based on the specified criteria like year, month, day, hours and location, the end-user is able to tune the minimum support threshold parameter intuitively using the fuzzy inference mechanism. Domain experts are can access several criteria, including minimum support threshold and number of accesses according to the user intuition, which is later, transformed into fuzzy inference parameters. We propose two different types of rule bases by considering the (support-minimum support, minimum support) and (support, minimum support), i.e., interval and case-based. To test our proposal, we used the web log dataset of the Department of Computer at the University of Calgary to analyze sequential access patterns of students during February and March carried out in the campus by taking the midterm dates into account. The results reported in this paper are promising; they demonstrate the applicability and effectiveness of the proposed approach.

Summary form only given. Replication is a technique in data grid environment that helps to reduce access latency and network bandwidth. Replication also increases data availability and thereby enhances the reliability of the system. Selecting the best replica depends on several factors such as past behavior of the transfer, current state of the network as well as the state of disk device. Here, we develop a predictive framework with a neural network that uses the data from various sources and predicts transfer bandwidth. We compare our results with regression models and demonstrate that the neural network technique outperforms the regression model based predictors for large file transfers.

Cluster validity investigates whether generated clusters are true clusters or due to chance. This is usually done based on subsampling stability analysis. Related to this problem is estimating true number of clusters in a given dataset. There are a number of methods described in the literature to handle both purposes. In this paper, we propose three methods for estimating confidence in the validity of clustering result. The first method validates clustering result by employing supervised classifiers. The dataset is divided into training and test sets and the accuracy of the classifier is evaluated on the test set. This method computes confidence in the generalization capability of clustering. The second method is based on the fact that if a clustering is valid then each of its subsets should be valid as well. The third method is similar to second method; it takes the dual approach, i.e., each cluster is expected to be stable and compact. Confidence is estimated by repeating the process a number of times on subsamples. Experimental results illustrate effectiveness of the proposed methods.

Unity is an architecture for integrating relational databases which performs three processes: meta-data capture, semantic integration, and query formulation and execution. The foundation of the architecture is a naming methodology that allows concepts to be integrated across systems. Semantic naming of schema constructs increases automation during integration and provides users with physical and logical access transparency during query formulation.

In this paper a visual tool for comparing different mountain pine-beetle management approaches is presented. This comparison tool gives the users the possibility of viewing their information, but more importantly it provides the ability to visually see the similarities and the differences between two simulations. This tool can help users to make decisions regarding their future actions against beetle attacks. The technique used is based on colour indexing combined with the mapping of the strategies into colour histograms. The system is entitled VICO from the phrase Visual Comparison.

Data warehousing is one of the major research topics of appliedside database investigators. Most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. Unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. This research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. The article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a “horizontal” fragmentation algorithm for the fact relation.

The World Wide Web (WWW) is a large distributed system consisting of multiple servers and clients. The paper briefly describes a model called WWW in DSM that provides a collaborative cache through an underlying distributed shared memory (DSM) paradigm. The model allows sharing of retrieved documents among browsers using the DSM. The authors have implemented a prototype system that shares documents among browsers in a local network, in an intranet, and across the Internet.

Most current Web applications perform operations that are basically read-only (retrieval of data/documents), so conflicts between transactions do not arise. However as Web applications increasingly become more sophisticated, permitting both concurrent data retrieval and update operations to remote databases, the potentials for conflicts between transactions becomes very high. Therefore, we must ensure correct transaction operation and the correct synchronization of concurrent transactions to avoid deadlock and ensure correctness. The paper presents a formal specification methodology based on the concept of concurrent execution that provides automatic communication and synchronization mechanisms between concurrent processes. The paper explores the issues of correctness, reliability, and recovery of data distributed across the enterprise, describes techniques for guaranteeing and enhancing correctness and procedures for recovering transactions and data in case of any transaction failures to guarantee a high degree of system dependability. Formal methods simplify the writing of concurrent programs by taking most of the burden of synchronization away from the programmer. Further specifying transaction systems formally, enhances application quality and reliability by verifying the functionalities of applications and the correctness of data.

Multidatabase serializability is defined as an extension of the well-known serializability theory in order to provide a theoretical framework for research in concurrency control of transactions over multidatabase systems. Also introduced are multidatabase serializability graphs which capture the ordering characteristics of global as well as local transactions. Two schedulers that produce multidatabase serializable histories are described. The first scheduler is a conservative one which only permits one global subtransaction to proceed if all of the global subtransactions can proceed for any given global transaction. The 'all or nothing' approach of this algorithm is simple, elegant, and correct. The second scheduler is more aggressive in that it attempts to schedule as many global subtransactions as possible as soon as possible. A distinguishing feature of this work is the environment that it considers; the most pessimistic scenario is assumed, where individual database management systems are totally autonomous with no knowledge of each other. This restricts the communication between them to be via the multidatabase layer and requires that the global scheduler 'hand down' the order of execution of global transactions.>