Abstract The release of ChatGPT to the general public has sparked discussions about the dangers of artificial intelligence (AI) among the public. The European Commission’s draft of the AI Act has further fueled these discussions, particularly in relation to the definition of AI and the assignment of risk levels to different technologies. Security concerns in AI systems arise from the need to protect against potential adversaries and to safeguard individuals from AI decisions that may harm their well-being. However, ensuring secure and trustworthy AI systems is challenging, especially with deep learning models that lack explainability. This paper proposes the concept of Controllable AI as an alternative to Trustworthy AI and explores the major differences between the two. The aim is to initiate discussions on securing complex AI systems without sacrificing practical capabilities or transparency. The paper provides an overview of techniques that can be employed to achieve Controllable AI. It discusses the background definitions of explainability, Trustworthy AI, and the AI Act. The principles and techniques of Controllable AI are detailed, including detecting and managing control loss, implementing transparent AI decisions, and addressing intentional bias or backdoors. The paper concludes by discussing the potential applications of Controllable AI and its implications for real-world scenarios.

Privacy by design requires that developers address privacy concerns from the early stage of software development life cycle. It encourages them to take a proactive approach to privacy engineering by identifying personal data, creating conceptual data flow diagrams, and identifying privacy threats. We argue that by providing a tool that automates some of the steps can reduce the burden on development teams. We develop a tool called PrivacyStory, including an end-to-end privacy requirement generation from a set of user stories. The tool provides some automation, utilizing a current state-of-the art natural language processing model. The core aim of our tool is to assist development teams in becoming more agile in their approach to privacy requirements engineering.

Attack graph-based methods are a promising approach towards discovering attacks and various techniques have been proposed recently. A key limitation, however, is that approaches developed so far are monolithic in their architecture and heterogeneous in their internal models. The inflexible custom data models of existing prototypes and the implementation of rules in code rather than declarative languages on the one hand make it difficult to combine, extend, and reuse techniques, and on the other hand hinder reuse of security knowledge – including detection rules and threat intelligence. KRYSTAL tackles these challenges by providing a knowledge graph-based, modular framework for threat detection, attack graph and scenario reconstruction, and analysis based on RDF as a standard model for knowledge representation. This approach provides query options that facilitate contextualization over internal and external background knowledge, as well as the integration of multiple detection techniques, including tag propagation, attack signatures, and graph queries. We implemented our framework in an openly available prototype and demonstrate its applicability on multiple scenarios of the DARPA Transparent Computing dataset. Our evaluation shows that the combination of different threat detection techniques within our framework improved detection capabilities. Furthermore, we find that RDF provenance graphs are scalable and can efficiently support a variety of threat detection techniques.

Security professionals rely extensively on log data to monitor IT infrastructures and investigate potentially malicious activities. Existing systems support these tasks by collecting log messages in a database, from where log events can be queried and correlated. Such centralized approaches are typically based on a relational model and store log messages as plain text, which offers limited flexibility for the representation of heterogeneous log events and the connections between them. A knowledge graph representation can overcome such limitations and enable graph pattern-based log analysis, leveraging semantic relationships between objects that appear in heterogeneous log streams. In this paper, we present a method to dynamically construct such log knowledge graphs at query time, i.e., without a priori parsing, aggregation, processing, and materialization of log data. Specifically, we propose a method that – for a given query formulated in SPARQL – dynamically constructs a virtual log knowledge graph directly from heterogeneous raw log files across multiple hosts and contextualizes the result with internal and external background knowledge. We evaluate the approach across multiple heterogeneous log sources and machines and see encouraging results that indicate that the approach is viable and facilitates ad-hoc graph-analytic queries in federated settings.

Quality distant education has received growing scientific attention in the recent decades, which is accelerated in present times as a result of the COVID-19 pandemic; traditional in-class teaching methods needed a rapid transformation to be effective in a distant educational context as well, to reduce the existing and deepening digital divide. Convinced by the "FAIR-principles" and the "CARE" Standard, the present paper searches for gamified applicational examples in forecasting and business studies. Through the discussion of the presently existing literature reviews in this domain, the article sheds light upon the development and current scientific standing of this research area. During the search for ethical implications, the present paper aims to formulate future considerations, believing in the idea that gamification, holding the possibility of successful individual outcomes for learners in knowledge acquisition and also in the form of psychological benefits, could serve as a tool for humanity to reach a global society where no one is left behind.

Collaborative filtering in a recommender system has a weakness called cold start problem. One way to resolve this problem is by using personality traits that can be automatically predicted from the status that the users write in social media like Facebook and Twitter. The problem with this method is that a user of such system must have at least one account in at least one social media and must write at least one status with certain length. We propose to use the combination of personality traits and demographic data to overcome this problem. Previous studies reveal that personality traits are influenced by age and gender. By using these findings, we will build models to predict personality traits from such demographic data. The modeling will be conducted by means of classification and association rule methods. Novel domains will be used in the proposed system, namely sports and hobbies.

The United Nations Agenda for Sustainable Development was unanimously adopted by all its 193 heads of member states or their representatives. "No-one should be left behind" is the ethical imperative of this development agenda. The agenda consists of seventeen Sustainable Development Goals (SDGs) with 169 targets. Unfortunately not even one of the (SDGs) is dedicated to ICT or Digitalization. Prima facie, the potential impact of ICT and digitalization is highly underestimated in the text of the 2030 Sustainable Development Agenda. This paper is aiming at pointing out the indispensable role of ICT for reaching the SDGs by 2030.

The growth of Linked Data has created a promising environment for data exploration and a growing number of tools allow users to interactively integrate data from various sources. Eliciting the reliability of the results of such ad-hoc integration processes, consistently recreating those results, and identifying changes upon re-execution, however, can be difficult. Automated process provenance trail creation can provide major benefits in this context, because (i) it enables users to trace the contribution of individual sources and processing steps to the final outcome and judge whether the result can be trusted; (ii) it ensures repeatability and raises the trustworthiness of results; (iii) it ideally enables reconstruction of Linked Data integration processes from the provenance information embedded in the final result. In this paper, we present a provenance model that facilitates automatic generation of semantic provenance information for generic Linked Data integration processes. We implement the generic model in a collaborative mashup environment and evaluate it by means of an example application. We find that the model provides a solid foundation for verifiability and contributes towards making Linked Data integration processes more open, transparent, and reusable, which is crucial in domains where the origin of data is essential, such as, for instance, statistical analyses, scientific research, and data journalism.

Despite an abundance of data available on the web today, satisfying users' complex information needs intelligently by automatically integrating and processing data from various sources remains challenging. In recent years, a large stream of research into mashups as a paradigm of end user development has emerged. These mashups foster combination and reuse of data and services and thereby allow end users to create novel applications. Developing such mashups efficiently and effectively, however, is still difficult for users that lack technical expertise. To address this issue, we extend a mashup platform with automatic mashup composition mechanisms and an agent that assists users in mashup design. To this end, we leverage semantics to simplify the mashup composition process on multiple levels. We associate each widget (i.e., mashup component) with a semantic model of inputs and outputs. These semantic models are helpful for identifying appropriate widgets in a given context and allow us to validate the links between widgets in a mashup. These validations provide the foundation for an advanced composition algorithm that automatically creates meaningful mashups from a given set of widgets. Finally, we develop an agent that leverages the semantic annotations to allow users to automatically compose mashups by entering natural-language text.

Today, public statistical data plays an increasingly important role both in public policy formation and as a facilitator for informed decision-making in the private sector. In line with to the increasing adoption of open data policies, the amount of data published by governments and organizations on the web is growing rapidly. To make such data more useful, the W3C has developed the RDF Data Cube Vocabulary to facilitate the publication of data in a more structured and interlinked manner. Although important first steps toward building a web of statistical linked datasets have been made, however, providing adequate facilities for end users to interactively explore and make use of the published data largely remains an unresolved challenge. This paper presents a widget-based approach to deal with this issue. In particular, we introduce a mashup platform that allows users without advanced skills and knowledge of Semantic Web technologies to interactively analyze datasets through widget compositions and visualizations. Furthermore, we provide mechanisms for the interconnection of datasets to support sophisticated knowledge extraction.

Whereas the number of open and accessible data sources on the web is growing rapidly, data becomes more heterogeneous and is difficult to use or reuse. Even though many national and international organizations have published their data according to the Linked Data principles, a considerable number of data sources is still available in traditional formats, e.g., HTML, XML, CSV, JSON. This results in a challenge of data aggregation and integration. To address this issue, we apply the visual programming paradigm to develop an open web platform. The platform is based on Semantic Web technologies and aims at encouraging and facilitating use of heterogeneous Open Data sources. We define Linked Widgets as user-driven modules which support users in accessing, processing, integrating, and visualizing different kinds of data. By connecting Linked Widgets from different developers, users without programming skills can compose and share ad-hoc applications that combine Open Data sources in a creative manner.

Using an existing decision support system in different data availability situations is a challenge. This is due to the significant variance in terms of what data can be acquired as input for such systems. In urban energy planning, the problem of data availability is even more crucial because of the large amount of data that is required. This problem affects the portability and the viability of such systems. i.e. they can only be used in one single specific situation at a certain point of time, as long as data are available in the same way. This paper presents an ontology-based approach for developing and keeping such systems more robust against data availability problems. The methodology allows integrating the initial requirements of the system, the domain semantics, and a multiple level-of-detail answering mechanism. The results presented in this paper are validated against an application in modeling a modular ontology-based urban energy planning support system. The proposed solution, in this paper, allows the flexibility of these systems in terms of input data. Furthermore, it allows better traceability of how the system fulfills the initial requirements.

Two-thirds of the overall primary energy in the world is consumed in cities, resulting in 71 percent of all energy related greenhouse gas emissions. There are many ongoing initiatives to develop strategies to lower the high amount of emissions at the level of cities. However, energy planners face problems that are related to the complexity of the urban energy systems, encapsulating the components of the city together with their properties and interactions that are relevant to the planning process. The complexity of the problem increases given the unavailability of data and their multiple levels of detail (LOD). Therefore, there is a need for adequate tools to support the development of integrated energy strategies, defining specific quantifiable CO2reduction measures to be implemented at the city level. In this research, we perform an analysis of urban energy planning processes. Then, we extract the general requirements of decision support in this discipline. Then, we perform a general data availability review in cities. This process results in formalizing a modular methodology for the development of urban energy planning support software. This methodology is specific to urban energy planning, it ensures the modularity of the development process, it leads to flexible software that operates under different available LODs of data, and it addresses the problem of data availability at the development stage.

Nowadays, the term sustainability gains more and more importance in corporate risk management and decision making. Corporate Sustainability Information Systems should support companies to analyse sustainability risks and provide relevant data in a practical manner. A major challenge in the domain of sustainability is to integrate environmental, social and economic data from different sources. This paper presents an approach for ontology-based data integration in the Corporate Sustainability domain preserving hierarchical relations to allow e.g. improved querying. Based on an existing sustainability framework defined in an XBRL-taxonomy, first a mechanism for the automatic generation of a domain-specific ontology is proposed. Finally, special data is queried and linked to the proposed ontology, serving as a working example for the suggested approach.

More and more governments publish Open Government Data (OGD) for their citizens, which receives great interest; because they are not only covering many domains, but also are meaningful and trustful. Since the data are not ready to be linked to other sources, some governments have started to follow the Linked Open Data paradigm as a mean to improve data quality. However, exploiting Linked Data is still a challenging task, which requires a profound understanding of underlying datasets, as well as considerable technical expertise. In this paper, we present an approach to remove the barrier between end users and Linked Data generally, and Open Government Linked Data particularly.

One of smartphones' core factor of success is the vast amount of available mobile applications. Despite this numerous number of apps there cannot exist one for every single use case. If a user does not find an app fulfilling the requirements, he rarely is able to write one of its own because of (1) missing programming skills, (2) too much time effort for simple tasks and (3) expenses on particular systems. This work proposes a mobile workflow engine on Android to empower users without much technical knowledge to build their solution in a platform-neutral and standardized language called XProc which is the W3C recommendation for data processing via XML technologies. For this purpose one of the XProc implementations is ported to Android and extended with smartphone specific features and services such as location, short messages, mobile UI, etc. Also a trigger mechanism has been implemented to activate workflows based on the user's context like time, location and reception of a short messages. A performance measurement done in this work demonstrates no execution drawback compared to native development after a workflow is compiled to its object representation.

Environmental Open Data has the potential to become a key strategy for the global endeavor of preserving the world's ecosphere. Therefore, this paper aims at researching the status quo of such data in Austria and exploring related advantages and shortcomings. First, the presented survey is motivated and put in perspective to relevant related work. Then, the approach to gaining the datasets is described and important parameters are specified. Subsequently, the results of the conducted survey are presented. Positive as well as negative highlights are pointed out. Many datasets with good features have been identified, however, there are still some drawbacks concerning data quality, diversity and content. Dealing with these issues and ultimately providing Linked Open Data would greatly improve the datasets and, ultimately, would allow for significantly better utilisation thereof. The goal of this work is to bring the concept of Open Data into the environmental domain by evaluating the environmental Open Data landscape in Austria.

Cloud computing introduces a fundamental shift in service delivery. A market registry, flexibility, exchangeability and integration of services are important issues for its success. In this work we introduce a unified Cloud and business service ontology with querying capabilities. Our framework addresses two aspects: on the one hand it gives answer to leading question how we can structure the term Cloud computing (as a question of basic research) and how we can enable a matching between offered Cloud services and demand. It closes the gap how we can summarize and describe Cloud services in a standardized way. On the other hand, it particularly addresses the demand for flexibility and exchangeability by the Cloud Computing paradigm and can serves as a repository of services.

The usefulness and ease of use of dashboards are essential elements in supporting interactive queries in data warehousing systems, as they provide the analysts the view of critical business metrics that reflect the business performance. However, on-demand dashboard development presents a challenging task for linking semantic equivalent data facts, discovering structural dependencies between and within data sources. In this paper, we introduce an improved approach for dashboard development that is supported by by-example framework and semantic-based data linkage management. In this Dashboard-by-example (DBE) framework, hypergraph-based techniques are adopted to acquire knowledge from heterogeneous and disparate data into homogeneous ontological clusters and partitions. Moreover, the use of hypergraph-guided data linkage provides multiple perspectives on the knowledge space, supporting interaction model to explore, contextualize and aggregate the data depending on the application needs. The applicability of our approach is discussed in a case study scenario, highlight the flexibility and efficiency in handling on-demand requirements in data warehousing systems.

Sharing personal information benefits both data providers and data consumers in many ways. Recent advances in sensor networks and personal archives enable users to record personal information including emails, social networking activities, or life events (life logging). These information objects are usually privacy sensitive and thus need to be protected adequately when being shared. In this work, we present a lightweight pseudonymization framework which allows users to benefit from sharing their personal information while still preserving their privacy. Furthermore, this approach increases the data owners' awareness of what information they are sharing, thus rendering data publishing more transparent.

In the last few years, web-based environments have witnessed the emergence of new types of on-demand business analysis that facilitate complex and integrated analytical information from multidimensional databases. In these on-demand environments, users of business intelligence architectures can have very different reporting and analytical needs, requiring much greater flexibility and adaptability of today's multidimensional data modeling. While structured data models for OLAP have been studied in detail, a majority of current approaches has not put its focus on the dynamic aspect of the multidimensional design and/or semantic enriched impact model. Within the scope of this paper, we present a flexible approach to model multidimensional databases in the context of dynamic web-based analysis and adaptive users' requirements. The introduced formal approach is based on hypergraphs with the ability to provide formal constructs specifying the different types of multidimensional elements and relationships which enable the support of highly customized business analysis. The introduced hypergraphs are used to formally define the semantics of multidimensional models with unstructured ad-hoc analytic activities. The proposed model also supports a formal representation of advanced concepts like dynamic hierarchies, many-to-many associations, additivity constraints etc. Some scenario example are also provided to motivate and illustrate the proposed approach.

Cloud Computing as the new delivery model for IT services is aiming at envisioning the abstraction of IT infrastructure and addressing the complexities of servers, applications, data and heterogeneous platforms. Nowadays the Cloud Computing Paradigm is being utilized by many small and medium enterprises that will get most of their computing resources from external providers. On the other hand the number of Cloud Services is rapidly growing with the potential of enabling the creation of customized based cloud services which address even sophisticated complex business and organizational requirements.

Whenever software is installed on a computer system, one has to agree to the end-user license agreement. The software license agreement grants licensee certain rights in software usage, but usually the ownership rights of the software stays with licensor. The licensor may also hold the right to restrict the usage of the software and can revoke the agreement if the licensee violates the license terms. Without agreeing with the license terms and conditions, the end-user is not authorized to use the software and could face penalties as described in the law. The main problem is that the percentage of the users who actually read the end-user license agreement which are almost incomprehensible for the average software user is very low. Mostly the end-user does not pay much attention to reading the license agreement because they believe that nearly all the end-user license agreements are practically the same. This misunderstanding makes them not fully read the agreement and just scroll the agreement and accept the terms and conditions. What they do not realize is that by breaking the license agreement they could be confronted with some penalties as described in the law. To overcome the problem of human inefficiency of understanding the license agreement, we have introduced a machine readable representation of the license agreement based on Semantic Web Technologies.

Trustable log data is essential in digital forensic investigations in order to allow reliable reconstruction of events. Existing solutions do not provide adequate protection, exposing the log-producing application to software-based attacks. In this paper we provide a solution based on Trusted Computing using a Trusted Platform Module (TPM) and AMD’s Secure Virtual Machine technology (SVM). While current solutions only protect against manipulation of existing logs, we go one step further by establishing hardware-based trust in the log producing application. Our solution ensures confidentiality, integrity and non-repudiation during creation, storage and transmission of log data.

In his seminal book "The New Asian Hemisphere - The irreversible shift of global power to the East" Kishore Mahbubani, the famous Singaporean diplomat and professor at the National University of Singapore gave an in-depth analysis about the changes which are implied by the use of mobile phones in developing countries. Three of his examples should illustrate this: (1) "The Bangladesh economy has gone up because of mobile phones by 2 percent. That surpasses the money given to Bangladesh through aid" , (2) "For less than one penny per minute the world cheapest cell phone call ratesIndian farmers in remote areas can check prices for their produce. They also track global trends using cell phones-based Internet services that show the price of pumpkins and bananas in London and Chicago." and as a third example (3) "Indian farmers use camera phones to snap pictures of crop pests, then send the photos by cell phone to biologists who can identify the bug" . He cited also the famous Indian born business professor C.K. Prahalad at the University of Michigan that one element of poverty is the lack of information and that cell phones give s poor people as much information as the middleman. For many fishermen in India who work off India's 4,350 miles of coastline monthly income has been tripled to average 150 US-Dollars since 2000 when cell phones began booming in India. The dealers are now forced to give more money because of competition.

Learning styles and affective states have a significant effect on student learning. The aim of this paper is to present a concept to identify and integrate learning styles and affective states of a learner into web-based learning management systems and therefore providing learners with adaptive courses and additional individualized pedagogical guidance that is tailored to their learning styles and affective states. Through considering affective states and learning styles, learners are provided with a learning environment that is more personalized and tailored to learners needs and current situation, leading to better learning outcomes and progress.

Illiteracy is still a dilemma, around more than 700 million adults in the world are unable to read and write. In the present modern society, technology is used to provide the techno-literacy solutions to help out the illiterates. Unfortunately, these solutions rarely consider the traditional learning theories and exploit two-dimensional (2D) platforms. Furthermore these techno-literacy solutions focus on desktop applications such as movies, recorded lectures on CDs and web-based application like 2D text-based instructional programs and games. These 2D applications are lacking the features such as self-presence, social-presence, situated-learning, embodied environment and learning by doing suggested in the traditional learning theories. There is a need to bridge the gap between technology-based solutions and traditional learning theories and to exploit emergent three-dimensional (3D) technologies to provide these lacking features. In this paper, we explore how the benefits of 3D emergent technologies like Second Life (SL) are exploited in coherence with traditional theories for Adult Basic Education (ABE). We present an immersive learning platform based on Multiple Intelligences (MI) Theory. We design and discuss an adaptive learning scenario for ABE in the SL. Finally we scrutinize the proposed platform to get an overview of the strengths and weaknesses in the intended area of application.

Using Distributed Hash Tables (DHT) for resource discovery in large-scale systems generates considerable maintenance overhead. This not only increases the bandwidth consumption but also affect the routing efficiency. In this paper, we deal with resource discovery while minimizing maintenance overhead in hierarchical DHT systems. The considered resources are metadata describing the data sources. In our solution, only one gateway in one overlay is attached to the superior level overlay. It aims to reduce both lookup and maintenance costs while minimizing the overhead added to the system. We present a cost analysis for a resource discovery process and discuss capabilities of the proposed protocol to reduce the overhead of maintaining the overlay network. The analysis result proved that our design decrease significantly the maintenance costs in such systems especially when nodes frequently join/leave the system.

The six papers in this special issue focus on availability, reliability, and security. Some of the topics covered include prevention of identity theft, biometric technology and authentication, and security considerations for RF identification.

Within the Austrian Grid project phase 2, three different groups, each allocated to a different work package, join their efforts to implement a grid infrastructure for the european research project "Breath Gas Analysis for molecular oriented diseases''. This position paper provides background on the task and the resulting requirements, a presentation on solutions developed during related projects in the application domain, identifies problems that have not yet been solved, and finally presents the intended solution to be developed.

Cloud computing allows delivering information technology power on demand. Be it either the hosting of a certain Web application or the outsourcing of an entire server or data center by means of virtualization. Applying these techniques however goes along with handing over the ultimate control of data to a third party. This paper investigates the application of Nimbus as a cloud resource and shows an example implementation for retaining data control to the user based on virtual machine images encrypted on the client side. This means that the procedures involved for verifying validity and accessing the virtual machine have to be entirely provided by the user. We provide a sample implementation of a secure virtual machine consisting of an encrypted partition, containing the data to be hosted, and a boot system, containing the logic to verify and access the encrypted partition. Further details of the implementation are described and applied on a cloud resource available within the AustrianGrid project. The methods presented in this paper form the basis for subsequent research on single point of access grid resp. cloud resources. The results will be applied in the AustrianGrid Phase 2 research project "Grid-supported Breath Gas Analysis of Molecular Oriented Diseases".

Bayesian networks are indispensable for determining the probability of events which are influenced by various components. Bayesian probabilities encode degrees of belief about certain events and a dynamic knowledge body is used to strengthen, update, or weaken these assumptions. The creation of Bayesian networks requires at least three challenging tasks: (i) the determination of relevant influence factors, (ii) the determination of relationships between the identified influence factors, and (iii) the calculation of the conditional probability tables for each node in the Bayesian network.Based on existing domain ontologies, we propose a method for the ontology-based generation of Bayesian networks. The ontology is used to provide the necessary knowledge about relevant influence factors, their relationships, their weights, and the scale which represents potential states of the identified influence factors.The developed method enables, based on existing ontologies, the semi-automatic generation and alternation of Bayesian networks.

In a learning environment, the students experience different affective states. Learning environments that takes into account the students' affective state enhance the students' learning, gain and experience. Therefore, it is crucial to provide students with different learning material and activities according to different affective states. To provide learning that considers students' affective states, the primary step is the detection of affective states of a student. In this paper, we present an approach for the detection of affective states from the patterns of students' behavior observed during an online course. By calculating the affective states and then filling that affective state data into the student model of a learning management system a basis for adaptivity is provided.

Advancement in Web Services has revolutionarized the way data is accessed and shared on the Web. Web Services are the new emerging means for efficient communication and information exchange. In the past, there was no proper means for communication and organizations used to exchange data without any proper medium which would give birth to privacy and security. This leads us to develop a system which is secure and trustworthy for the end-user. Personal Web Services (PWS) is the new emerging field which provides user with high level of personalization e.g. a person traveling to Austria can use his/her handheld device to locate nearby restaurants, tourist places, accommodation, medical stores and transportation system etc. Personalized services are based on the user profile which contains user preferences (what he/she likes and dislikes, interests, behavior in different situations), considered as sensitive data.To acquire any service, the requester has to disclose personal information (e.g. name, address, phone number, present location etc) to utilize that service. The most important of them is the user context, i.e. location. Misuse of user's sensitive and other identifiable information could lead to threat to user privacy. While negotiating with the service provider, the user gives away important information, but what does not realize is that disclosing this information could be a threat to his/her privacy. The service provider could share this important information with third parties or could use it for commercial reasons. In a collaborative environment, it is necessary that unauthorized people should not have access to sensitive information.In this paper we propose a solution to empower the end-user to selectively control who has access to their contextual information and under which conditions based on ontologies using SemanticLIFE's Personal Services. SemanticLIFE is a personal information management system which manages and retrieves the lifetime's information while giving annotations to emails, browsed Web pages, phone calls, images, contacts and other resources of individuals. It enables the acquisition and storage of data in ontological form in semantic store. Each source of contextual information is represented as Personal Web Service.

The rise in interconnectivity in the last few years has made computer systems and networks more vulnerable to threats as they are accessed by an ever increasing number of users. Nowadays organizations are lacking proper security measures and means to calculate risk assessment for their assets. Legacy systems in organizations are facing different kind of risks like viruses, bugs and system failure causing damages to hardware and software resulting in data loss. The ultimate challenge in many organizations is to assess their risk factors for their computers and networks. There is no way to completely overcome the threat that an organization might have. The goal is to calculate risks, so that problems resulting from them could be minimized and to fill the gap between business entities (like a project, a role) and organization infrastructure using semantic Web technologies. SemanticLIFE is a personal information management system which gathers the user interaction events and correlates those by using ontologies. In this paper the ontology-based risk assessment in the context of the organizational security, a fundamental issue for planners and decision makers in the IT field, is explored using SemanticLIFE tool

Project management traditionally has a strong focus on human reporting that fits well a tightly coupled form of organization to ensure the quality of project reporting. For loosely coupled forms of organization, such as open source systems (OSS) development projects, there are very few approaches to ensure the quality of project reporting; a promising approach can be to augment human reporting with data analysis based on the communication and state changes in an OSS project. In this paper we propose a concept and an initial measurement approach for event-based monitoring of OSS projects to better understand the actual benefit of tool-supported gathering, correlating and analyzing processes event data from the OSS community as a supplement for traditional software project monitoring data collection. We report on an empirical feasibility study investigating success and risk indicators of five OSS projects listed in the Apache Incubator

Healthcare organisations practicing evidence-based medicine strive to unite their data assets in order to achieve a wider knowledge base for more sophisticated research as well as to provide a matured decision support service for the care givers. The central point of such an integrated system is a data warehouse, to which all participants have access. Due to the high confidentiality of healthcare data, and the privacy policy of participating organisations, the proposed warehouse is not created physically but as a federated system. Its conceptual model is based on a widely accepted international standard to overwhelm the heterogeneity of the components. Any disclosure of health data, especially when related to a particular person, could be irreparably harmful, and their protection is even legally prescribed. Depersonalisation and pseudonymisation are used to ensure that personal identities are made secret before sending data to the federation. In this paper a case study of a federation of health insurance data warehouses (HEWAF) is described. The protection of data privacy and confidentiality in the underlying warehouse is guaranteed through reliable security measures in the federation.

Relocating the code for data preprocessing (DPP) closer towards the data source is the overall task of the D/sup 3/G framework (data statistics, data understanding, data preprocessing on the grid), developed within a joint project of the University of Vienna, the Vienna University of Technology and the Czech Technical University. This paper presents the data service side architecture to gather data statistics on-the-fly and use them in remote DPP methods on query results as well as an approach to gather exact continuous data statistics for whole tables in a database on the grid. The performance results of our prototype implementation are showing low running costs for the continuous data statistics inside the database and also the feasibility of our proposed data service side functionality.

This paper presents a new approach in guiding users to formulate unambiguous queries based on their common nature of asking for information. The approach known as the "front-end approach" gives users an overview about the system data through a "virtual data component" which stores the merged metadata of data storage sources. Based on this component, users are aware of their stored data while generating requests. This approach reduces the ambiguities in users' requests at very early stage; and this makes the query refinement process easily fulfill users' demands

In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

The dynamic business environment of many organizations require massive monitoring of their processes in real-time in order to proactively respond to exceptional situations and to take advantage of time-sensitive business opportunities. The ability to sense and interpret events about a changing business environment requires an event-driven IT infrastructure for pwerforming fast and well-informed decisions and putting them into action. However, traditional Business Intelligence (BI) and Data Warehousing technologies do not directly address time sensitive monitoring and analytical requirements. We introduce an enhanced BI architecture that covers the complete process to sense, interpret, predict, automate and respond to business environments and thereby aims to decrease the reaction time needed for business decisions. We propose an event-driven IT infrastructure to operate BI applications which enable real-time analytics across corporate business processes, notifies the business of actionable recommendations or automatically triggers business operations, and effectively closing the gap between Business Intelligence systems and business processes. A scenario from the area of mobile phone fraud detection was chosen for building a prototype that illustrates the proposed approach by using current available IT technologies.

The grid is considered as a crucial technology for the future knowledge-based economy and science. The Wisdom Grid project (a joint research effort of the University of Vienna and the Vienna University of Technology) aims, as the first research effort, to cover all aspects of the knowledge life cycle on the grid - from discovery in grid data repositories, to processing, sharing and finally reusing of knowledge as input for a new discovery. This paper first outlines the architecture of the Wisdom Grid infrastructure and then focuses on the kernel architecture component called Grid-Miner, which realizes the knowledge discovery, based on data mining and on-line analytical processing (OLAP) in grid repositories. A running GridMiner prototype is already available to the scientific community as an open service system.

Multi agent systems, grid technology, semantic Web, and Web intelligence paradigm are three modern approaches in information technologies, which we put together in our research effort described to create a new-generation infrastructure called the wisdom grid with the mission to maintain, share, discover, and expand knowledge in geographically distributed environments, and in this way to support development of intelligent information systems. We introduce motivating ideas for this project, proposes the system architecture of an instance of the wisdom grid. The full version also describe the wisdom grid functionality by means of a case study of one medical application

The paper introduces the metadata management concept based on the object oriented paradigm for modeling and querying multidimensional OLAP data. In this model we apply the basic concepts of the object oriented model including object, class, and relationship between objects to describe objects of multidimensional data and the OLAP operations. In our approach the multidimensional OLAP data is modeled as a fact and a set of dimensions that are organized into class hierarchy.

Article Free Access Share on A multidimensional modeling approach for OLAP within the framework of the relational model based on quotient relations Authors: O. Mangisengi Institute of Software Technology, Technical University of Vienna, Austria Institute of Software Technology, Technical University of Vienna, AustriaView Profile , A. M. Tjoa Institute of Software Technology, Technical University of Vienna, Austria Institute of Software Technology, Technical University of Vienna, AustriaView Profile Authors Info & Claims DOLAP '98: Proceedings of the 1st ACM international workshop on Data warehousing and OLAPNovember 1998 Pages 40–46https://doi.org/10.1145/294260.294270Published:01 November 1998Publication History 1citation445DownloadsMetricsTotal Citations1Total Downloads445Last 12 Months11Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Software development often suffers from well-known problems such as wrong schedules and cost estimations, low productivity, and low product quality. In order to overcome these problems we suggest adapting the concepts of "organizational memory" and "organizational learning" and we argue in favor of establishing a reuse culture of software process models. We introduce an approach based on the process definition/instantiation/enaction paradigm and on the reuse of explicit software process descriptions (process models). The key features of our approach are the division of process descriptions into a goal-oriented process definition document and a formal implementation-oriented process model on the one hand and the use of an artificial neural network, more precisely a self-organizing map, for classification and retrieval purposes on the other. We present an exposition of our approach and discuss the promising results of an experiment in structuring a software process library and retrieving reuse candidates for upcoming projects.

The paper describes authorization and access control in the IRO-DB database system, a system supporting interoperable access between relational and object oriented databases. The security policy developed is a federated administrative discretionary access control policy which supports positive, negative, as well as implied authorization, includes a procedure for conflict resolution within the set of specified authorization rules, and concentrates on role based security.

Properly structured software libraries are crucial for the success of software reuse. Specifically, the structure of the software library ought to reflect the functional similarity of the stored software components in order to facilitate the retrieval process. We propose the application of artificial neural network technology to achieve such a structured library. In more detail, we utilize an artificial neural network adhering to the unsupervised learning paradigm. The distinctive feature of this very model is to make the semantic relationship between the stored software components geographically explicit. Thus, the actual user of the software library gets a notion of the semantic relationship between the components in terms of their geographical closeness.>

This paper proposes building a prototyping environment as part of the standard design process of multilevel secure database applications. For this paper we see the following contributions: First, based on a careful study of multilevel security requirements we developed a security constraints language (SCL) for specifying application dependent database security semantics. Second, we implemented SCL by using the deductive database systems LDL thereby offering a useful toolset with which a prototype of a multilevel secure relational database application can efficiently be developed. Prototyping will help the database designer to arrive at a consistent data classification and at a satisfactory database design.>

This paper classifies a collection of semantic data models within a framework consisting of the following comparison-criteria: classification, aggregation, generalization and association (resp. cover aggregation).