Showing papers presented at "International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management in 2010"

A secure and efficient order preserving encryption scheme for relational databases

Abstract: Encryption is a well-studied technique for protecting the confidentiality of sensitive data. However, encrypting relational databases affects the performance during query processing. Preserving the order of the encrypted values is a useful technique to perform queries over the encrypted database with a reasonable overhead. Unfortunately, the existing order preserving encryption schemes are not secure against known plaintext attacks and statistical attacks. In those attacks, it is assumed that the attacker has prior knowledge about plaintext values or statistical information on the plaintext domain. This paper presents a novel database encryption scheme called MV-POPES (Multivalued Partial Order Preserving Encryption Scheme), which allows privacy-preserving queries over encrypted databases with an improved security level. Our idea is to divide the plaintext domain into many partitions and randomize them in the encrypted domain. Then, one integer value is encrypted to different multiple values to prevent statistical attacks. At the same time, MV-POPES preserves the order of the integer values within the partitions to allow comparison operations to be directly applied on encrypted data. Our scheme is robust against known plaintext attacks and statistical attacks. MV-POPES experiments show that security for sensitive data can be achieved with reasonable overhead, establishing the practicability of the scheme.

A Pervasive Approach to a Real-Time Intelligent Decision Support System in Intensive Medicine

Abstract: The decision on the most appropriate procedure to provide to the patients the best healthcare possible is a critical and complex task in Intensive Care Units (ICU). Clinical Decision Support Systems (CDSS) should deal with huge amounts of data and online monitoring, analyzing numerous parameters and providing outputs in a short real-time. Although the advances attained in this area of knowledge new challenges should be taken into account in future CDSS developments, principally in ICUs environments. The next generation of CDSS will be pervasive and ubiquitous providing the doctors with the appropriate services and information in order to support decisions regardless the time or the local where they are. Consequently new requirements arise namely the privacy of data and the security in data access. This paper will present a pervasive perspective of the decision making process in the context of INTCare system, an intelligent decision support system for intensive medicine. Three scenarios are explored using data mining models continuously assessed and optimized. Some preliminary results are depicted and discussed.

A Connection between Extreme Learning Machine and Neural Network Kernel

Abstract: We study a connection between extreme learning machine (ELM) and neural network kernel (NNK). NNK is derived from a neural network with an infinite number of hidden units. We interpret ELM as an approximation to this infinite network. We show that ELM and NNK can, to certain extent, replace each other. ELM can be used to form a kernel, and NNK can be decomposed into feature vectors to be used in the hidden layer of ELM. The connection reveals possible importance of weight variance as a parameter of ELM. Based on our experiments, we recommend that model selection on ELM should consider not only the number of hidden units, as is the current practice, but also the variance of weights. We also study the interaction of variance and the number of hidden units, and discuss some properties of ELM, that may have been too strongly interpreted previously.

Semantics and Machine Learning: A New Generation of Court Management Systems

Abstract: The progressive deployment of ICT technologies in the courtroom, jointly with the requirement for paperless judicial folders pushed by e-justice plans, are quickly transforming the traditional judicial folder into an integrated multimedia folder, where documents, audio recordings and video recordings can be accessed via a web-based platform. Most of the available ICT toolesets are aimed at the deployment of case management systems and ICT equipment infrastructure at different organisational levels (court or district). In this paper we present the JUMAS system, stemmed from the homonymous EU project, that instead takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of the multimedia judicial folders. JUMAS provides not only a streamlined content creation and management support for acquiring and sharing the knowledge embedded into judicial folders but also a semantic enrichment of multimedia data for advanced information retrieval tasks.

Requirements and modelling of a workspace for tacit knowledge management in railway product development

Abstract: Our work is focused on finding ways to foster tacit knowledge in the context of a company providing railway transport solutions. Our study refers to a specific process of railway product development, context in which, we consider the problem of tacit knowledge as a concern of organisational learning. We present our findings on the requirements and modelling of a workspace supporting tasks of the process considered and sustaining both organisational learning and tacit knowledge management.

Evaluating Dynamic Ontologies

Abstract: Ontology evaluation poses a number of difficult challenges requiring different evaluation methodologies, particularly for a “dynamic ontology” generated by a combination of automatic and semi-automatic methods. We review evaluation methods that focus solely on syntactic (formal) correctness, on the preservation of semantic structure, or on pragmatic utility. We propose two novel methods for dynamic ontology evaluation and describe the use of these methods for evaluating the different taxonomic representations that are generated at different times or with different amounts of expert feedback. These methods are then applied to the Indiana Philosophy Ontology (InPhO), and used to guide the ontology enrichment process.

Optimization techniques for range queries in the multivalued-partial order preserving encryption scheme

Abstract: Encryption is a well-studied technique for protecting the privacy of sensitive data. However, encrypting relational databases affects the performance during query processing. Multivalued-Partial Order Preserving Encryption Scheme (MV-POPES) allows privacy preserving queries over encrypted databases with reasonable overhead and an improved security level. It divides the plaintext domain into many partitions and randomizes them in the encrypted domain. Then, one integer value is encrypted to different multiple values to prevent statistical attacks. At the same time, MV-POPES preserves the order of the integer values within the partitions to allow comparison operations to be directly applied on encrypted data. However, MV-POPES supports range queries at a high overhead. In this paper, we present some optimization techniques to reduce the overhead for range queries in MV-POPES by simplifying the translated condition and controlling the randomness of the encrypted partitions. The basic idea of our approaches is to classify the partitions into many supersets of partitions, then restrict the randomization within each superset. The supersets of partitions are created either based on predefined queries or using binary recursive partition. Experiments show high improvement percentage in performance using the proposed optimization approaches. Also, we study the affect of those optimization techniques on the privacy level of the encrypted data.

ONTOLOGY-BASED RAILWAY INFRASTRUCTURE VERIFICATION - Planning Benefits

Abstract: Planning new railway infrastructures is a complex process. We present an approach where the formalization of expert knowledge regarding the railway domain is motivated in order to improve the planning process. By applying ontologies as a representation of railway related knowledge we are able to make the coherencies of infrastructural elements explicit. Furthermore the integration of an ontology-based rule language provides the possibility of a semi-automated integrity verification of static infrastructure and safety components. Semantical inconsistencies potentially leading to unsafe conditions regarding train operations can be spotted within this verification process. This combination of conceptualization and correlation rules tends to be applicable for the creation of a formal and consistent model of specific railway infrastructures which are to be planned.

CRIMEFIGHTER ASSISTANT - A Knowledge Management Tool for Terrorist Network Analysis

Abstract: A terrorist network is a special kind of social network with emphasis on both secrecy and efficiency. Such networks (consisting of nodes and links) need to be analyzed and visualized in order to gain a deeper knowledge and understanding that enables network destabilization. Previous research on terrorist network analysis has to a large degree focused on analysis of nodes. This paper presents the CrimeFighter Assistant, a novel knowledge management tool for terrorist network analysis. CrimeFighter Assistant treats links as first class objects and provides a better balance between network, node, and link analysis.

A Workspace to Manage Tacit Knowledge Contributing to Railway Product Development during Tendering

Abstract: Railway products development during the Tendering phase is very challenging and tacit knowledge of experts remains the key of its success. Therefore, there is a need to capture and preserve tacit knowledge used during Tendering in order to improve, in time, railway products development. In this context, we think that tacit knowledge management and organizational learning need to be intertwined and supported by a dedicated workspace. In this paper, we propose the model and the implementation of such a workspace.

Automated Reasoning Support for Ontology Development

Abstract: The design and evaluation of ontologies in first-order logic poses many challenges, many of which focus on the specification of the intended models for the ontology’s concepts and the relationship between these models and the models of the ontology’s axioms. In this paper we present a methodology for the verification of first-order logic ontologies, and provide a lifecycle in which it may be implemented to develop a correct ontology. Automated reasoning plays a critical role in the specification of requirements, design, and verification of the ontology. The application of automated reasoning in the lifecycle is illustrated by examples from the PSL Ontology.

Developing argument assistant systems from a usability viewpoint

Abstract: This paper discusses the role of usability as a quality key attribute for the deployment of Argument Assistant Systems, which are software tools intended to provide effective knowledge management facilities when solving problems in different contexts, helping to identify, create, represent and analyze the arguments involved as well as their interrelationships. Based on a reverse engineering process, a set of usability-oriented Design Guidelines were identified and instantiated for the Argument Assistant System domain. Besides, some usability principles are proposed and linked to every suggested guideline to evaluate its quality in use.

Modeling the International Classification of Diseases (ICD-10) in OWL

Abstract: Current efforts in healthcare focus on establishing interoperability and data integration of medical resources for better collaboration between medical personal and doctors, especially in the patient treatment process. In covering human diseases, one of the major international standards in clinical practice is the International Classification for Diseases (ICD), maintained by the World Health Organization (WHO). Several country- and language-specific adaptations exist which share the general structure of the WHO version but differ in certain details. This complicates the exchange of patient records and hampers data integration across language borders. We present our approach for modeling the hierarchy of the ICD-10 using the Web Ontology Language (OWL). OWL, which we will introduce shortly, should provide a formal ontological basis for ICD-10 with enough expressivity to model interoperability and data integration of several medical resources such as ICD. Our resulting model captures the hierarchical information of the ICD-10 as well as comprehensive class labels for English and German. Specialities such as “Exclusion” statements, which make statements about the disjointness of certain ICD-10 categories, are modeled in a formal way. For properties which exceed the expressivity of OWL-DL, we provide a separate OWL-Full component which allows us to use the hierarchical knowledge and class labels with existing OWL-DL reasoners and capture the additional information in a Semantic Web format.

Kanban sharing and optimization in bosch production system

Abstract: Information sharing and optimization is a key factor for effective knowledge management, which is based on data exchange, communication and technological infrastructures and standardization, being essential in order to remain competitive in the today’s global market scenario. In this context, human functions are also relevant, and in this work we refer to the interaction of both in the optimization of Bosch Production System. Therefore, the aim of this paper consists on presenting the minimization of kanbans, when sharing this information in a proposed cellular manufacturing environment in Bosch production, in order to enhance workflows and material and work in process management as well as human interactions and production performance and productivity.

Incremental user modeling with heterogeneous user behaviors

Abstract: We describe an innovative approach for incrementally learning user interests from multiple types of user behaviours or events. User interests are reflected in the concepts and their relations contained in these events. The concepts and relations form the structural elements of a user interest model. The relevance of each structural element is signified by a weight. Our user modeling algorithm builds dynamic user interest model with two concurrent processes. One process grows the model by intelligently incorporating concepts and relationships extracted from user events. Another process adapts the weights of these model elements by applying a novel combination of two mechanisms: reinforcement and forgetting, both important in modulating user interests. Our modeling algorithm supports incremental and real time modeling, and readily extends to new types of user events. One interesting application of user interest models is to identify a virtual interest group (VIG), which is an ordered set of other system users who exhibit interests similar to those of the target user. As a result, we can evaluate our user modeling algorithm through a VIG identification task. In a formative NIST evaluation using intelligence analysts, we achieved 95% VIG identification precision and recall.

Knowledge Management Tools for Terrorist Network Analysis

Abstract: A terrorist network is a special kind of social network with emphasis on both secrecy and efficiency. Such networks (consisting of nodes and links) needs to be analyzed and visualized in order to gain a deeper knowledge and understanding that enable network destabilization. This paper presents two novel knowledge management tools for terrorist network analysis. CrimeFighter Investigator provides advanced support for human-centered, target-centric investigations aimed at constructing terrorist networks based on disparate pieces of terrorist information. CrimeFighter Assistant provides advanced support for network, node, and link analysis once a terrorist network has been constructed. The paper focuses primarily on the latter tool.

Inference Based Query Expansion Using User’s Real Time Implicit Feedback

Abstract: Query expansion is a commonly used technique to address the problem of short and under-specified search queries in information retrieval Traditional query expansion frameworks return static results, whereas user’s information needs is dynamics in nature User’s search goal, even for the same query, may be different at different instances This often leads to poor coherence between traditional query expansion and user’s search goal resulting poor retrieval performance In this study, we observe that user’s search pattern is influenced by his/her recent searches in many search instances We further propose a query expansion framework which explores user’s real time implicit feedback provided at the time of search to determine user’s search context and identify relevant query expansion terms From extensive experiments, it is evident that the proposed query expansion framework adapts to the changing needs of user’s information need

Applying fusion techniques to graphical methods for knowledge based processing of product use information

Abstract: In this paper the processing and modelling of product use information raised by graphical methods on the basis of a praxis and application scenario. Product Lifecycle Management (PLM) ensures a uniform data basis for supporting numerous engineering and economic organisational processes along the entire product life cycle – from the first product idea to disposal or recycling of the product respectively. The Product Use Information (PUI) -e.g. condition monitoring data, failures or incidences of maintenanceof many instances of one product type is generated in the product use phase. The processing and modelling of PUI raised by graphical methods like Bayesian Networks. In accordance, the product use knowledge leads back to the product development phase and is used for discovering room for product improvements of the next product generation. Therefore the PUI of the different instances should be aggregated by applying fusion techniques to deduce/achieve generalized product improvements for a product type. As a result this paper reveals a novel approach of applying new feedback mechanism of PLM for product improvements.

Semantics for Enhanced Email Collaboration

Abstract: Digital means of communications such as email and IM have become a crucial tool for collaboration. Taking advantage of the fact that information exchanged over these media can be made persistent, a lot of research has strived to make sense of the ongoing communication processes in order to support the participants with their management. In this Chapter we pursue a workflow-oriented approach to demonstrate how, coupled with appropriate information extraction techniques, robust knowledge models and intuitive user interfaces; semantic technology can provide support for email-based collaborative work. While eliciting as much knowledge as possible, our design concept imposes little to no changes, and/or restrictions, to the conventional use of email.

Process-centric enterprise workspace based on semantic wiki

Abstract: We describe the design of a process-centric solution for a specific enterprise process, proposal development, in a large consulting company. The solution is based on a semantic wiki and aimed at capturing informal knowledge processes. It improves collaboration while allowing proposal managers to allocate, track, and manage the work of development teams. We motivate our system by data gathered from more than 60 potential users and validate the approach through usability tests. We discuss technical and acceptance issues as well as future steps necessary to maximize deployment of the system.

Semantics and machine learning for building the next generation of judicial court management systems

Abstract: Information and Communication Technologies play a fundamental role in e-justice: the traditional judicial folder is being transformed into an integrated multimedia folder, where documents, audio and video recordings can be accessed and searched via web-based judicial content management platforms. Usability of the electronic judicial folders is still hampered by traditional support toolset, allowing search only in textual information, rather than directly in audio and video recordings. Transcription of audio recordings and template filling are still largely manual activities. Thus a significant part of the information available in the trial folder is usable only through a time consuming manual search especially for audio and video recordings that describe not only what was said in the courtroom, but also the way and the specific trial context in which it was said. In this paper we present the JUMAS system, stemming from the JUMAS project started on February 2008, that takes up the challenge of using semantics towards a better usability of the multimedia judicial folders. The main aim of this paper is to show how JUMAS has provided the judicial users with a powerful toolset able to fully exploit the knowledge embedded into multimedia judicial folders.

A Clinical Application of Feature Selection: Quantitative Evaluation of the Locomotor Function

Abstract: Evaluation of the locomotor function is important for several clinical applications (eg fall risk of the elderly, characterization of a disease with motor complications) We consider the Timed Up and Go test which is widely used to evaluate the locomotor function in Parkinson’s Disease (PD) Twenty PD and twenty age-matched control subjects performed an instrumented version of the test, where wearable accelerometers were used to gather quantitative information Several measures were extracted from the acceleration signals; the aim is to find, by means of a feature selection, the best set that can discriminate between healthy and PD subjects A wrapper feature selection was implemented with an exhaustive search for subsets from 1 to 3 features A nested leave-one-out cross validation (LOOCV) was implemented, to limit a possible selection bias With the selected features a good accuracy is obtained (75% of misclassification rate) in the classification between PD and healthy subjects

INTEGRATING CURRENT PRACTICES AND INFORMATION SYSTEMS IN KM INITIATIVES - A Knowledge Management Audit Approach

Abstract: Researchers and practitioners in the field of knowledge management have observed the need of performing studies to understand the context and specific knowledge workers’ needs before proposing strategies or systems that may not be entirely useful for organizations, resulting in costly and unsuccessful knowledge management projects. Different approaches have been proposed to face this problem, such as process engineering techniques to integrate knowledge management in business processes, and also knowledge audits to identify the knowledge and knowledge problems in organizations. This paper draws on the idea of the knowledge audit to propose a methodology for knowledge management audits, which integrates process engineering techniques and the main tasks of knowledge audits. The methodology was developed based on one of our previous works, literature review, and our own experience in field studies. The methodology, its constitutive phases and main tasks, together with some aspects about its use in field studies, including benefits and weaknesses, are described.

Modeling Knowledge Flows in Software Project Management Processes.

Abstract: Software development SMEs interested in launching a KM initiative or software project managers working on taking their KM initiative to the next level, need to assess what strategy will best fit their knowledge needs and which will be the most likely to succeed based on their social, cultural and technological aspects. Taking into account the social and cultural characteristics of Mexican software development SMEs, the Mexican Ministry of the Economy encouraged the creation and adoption of the NMX-I-059-NYCE-2005 Standard. The main goal of this standard is help SMEs become more competitive and reach higher maturity levels. However, SMEs adopting or implementing this standard sometimes experience difficulties and problems in their daily software activities. In this paper, we model ongoing knowledge flows as an adaptation to Choo’s framework, applied to the project portfolio management process as defined in such standard. In addition, we present some strategies followed by Mexican software development SMEs while conducting a SPI program for maturity levels 1 and 2.

Testing and Improving the Performance of SVM Classifier in Intrusion Detection Scenario

Abstract: Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network Anomaly discovery has attracted the attention of many researchers to overcome the disadvantage of signature-based IDSs in discovering complex attacks Although there are some existing mechanisms for Intrusion detection, there is need to improve the performance Machine Learning techniques are a new approach for Intrusion detection and KDDCUP’99 is the mostly widely used data set for the evaluation of these systems The goal of this research is using the SVM machine learning model with different kernels and different kernel parameters for classification unwanted behavior on the network with scalable performance Also elimination of the insignificant and/or useless inputs leads to a simplification of the problem, faster and more accurate detection may result This work also evaluates the performance of other learning techniques (Filtered J48 clustering, Naive Bayes) over benchmark intrusion detection dataset for being complementary of SVM The model generation is computation intensive; hence to reduce the time required for model generation various different algorithms Various algorithms for cluster to class mapping and instance testing have been proposed to overcome problem of time consuming for real time detection I show that our proposed variations matured in this paper, contribute significantly in improving the training and classifying process of SVM with high generalization accuracy and outperform the enhanced technique

KNOWLEDGE MANAGEMENT USING CLUSTERS IN VANETS - Description, Simulation and Analysis

Abstract: Vehicular Ad Hoc Network (VANET) is a type of mobile network that allows Knowledge Management of the road providing communication among nearby vehicles and between vehicles and nearby fixed roadside equipment. The lack of centralized infrastructure and high node mobility and number of vehicles generate problems such as interrupting connections, difficult routing, security of communications and scalability. Clusters are here proposed as a solution to avoid data collisions by decreasing the number of connections exchanged among vehicles and to reach this goal, nodes must cooperate, and form or join clusters depending on their state. This paper provides a global vision of the life cycle of cooperative nodes who form clusters and a description of how to deal with the information within a cluster. Simulation results show that the proposed scheme reduces the number of communications, avoiding data loss due to collisions. This paper provides a full description of each cluster management process and of how to deal with the information within a cluster.

Knowledge Sharing in the First Aid Domain through End-User Development

Abstract: The paper addresses the knowledge sharing needs of an Italian non- profitassociationforfirstaid.Theirvolunteers,andparticularly ambulance drivers, need to know the territory to provide first aid quickly and in a safe manner. This knowledge is often tacit and distributed. Paper-based maps are currently the means to spread and share knowledge among volunteers, while training sessions regularly provide information about holdups and fast routes to a place. The paper describes FirstAidMap, a collaborative web mapping system we have designed with volunteers to satisfy their needs. The system beyond supporting the training activity of ambulance drivers provides an interactive space that all volunteers can directly shape to build and share their knowledge about the territory. FirstAidMap integrates proper end-user development functionalities to engage and motivate volunteers to participate in map shaping, thus evolving from passive users to co-designers of map content. Results of a preliminary evaluation are discussed.

Supporting digital collaborative work through semantic technology

Abstract: Taking advantage of the fact that knowledge exchanged within digital working environments can be made persistent, a lot of research has strived to make sense of the ongoing communications in order to support the participants with their shared management. Semantic technology has been applied for the purpose as it ensures a shared understanding of the underlying collaboration, between both humans and machines. In this paper we demonstrate how, coupled with appropriate information extraction techniques, robust knowledge models and intuitive user interfaces; semantic technology can provide support for digital collaborative work. As a virtual working environment, e-mail was a natural contender for testing our hypothesis. Taking a workflow management-based approach, we demonstrate how semantics can indeed support email-based collaboration via Semanta – a tool extending popular email clients enabling semantic email. In particular we present a novel workflow-based email visualisation, the tool’s summative evaluation, and discuss the odds of semantic applications like Semanta evolving beyond research prototypes.

An Architecture to Support Semantic Enrichment of Knowledge Sources in Collaborative Engineering Projects

Abstract: This work brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process, aiming to support collaborative work carried out by project teams, through an ontology-based platform and a set of knowledge-enabled services. We introduce the conceptual approach, the technical architectural (and its respective implementation) supporting a modular set of semantic services based on individual collaboration in a project-based environment (for Building & Construction sector). The approach presented here enables the semantic enrichment of knowledge sources, based on project context. The main elements defined by the architecture are an ontology (to encapsulate human knowledge), a set of web services to support the management of the ontology and adequate handling of knowledge providing search/indexing capabilities (through statistical/semantically calculus), providing a systematic procedure for formally documenting and updating organizational knowledge. Results achieved so far and future goals pursued here are also presented.