Serverless computing allows users to run code without managing servers, offering automatic scaling and pay-as-you-go pricing. AWS Lambda, the first major commercial serverless platform to offer ARM support alongside traditional x86, enables a direct comparison of the two architectures. In this study, we first analyze the architectural differences between ARM and x86 to understand potential performance and cost implications. We then empirically evaluate both architectures using a set of real-world serverless benchmarks, measuring execution time, response time, peak memory usage, cold start times, total cost, and performance-to-cost ratio. Our results show that ARM consistently delivers lower cost across all workloads and memory configurations, with savings of up to 30%, while performance differences vary by workload and memory allocation. Based on our findings, we provide recommendations for users who consider migrating their AWS Lambda functions from x86 to the ARM architecture.

Serverless computing enables developers to deploy code without managing infrastructure, but suffers from cold start overhead when initializing new function instances. Existing solutions such as “keep-alive” or “pre-warming” are costly and unreliable under bursty workloads. We propose universal workers, which are computational units capable of executing any function with minimal initialization overhead. Based on an analysis of production workload traces, our key insight is that requests in Function-as-a-Service (FaaS) platforms show a highly skewed distribution, with most requests invoking a small subset of functions. We exploit this observation to approximate universal workers through locality groups and three-tier caching (handler, install, import). With this work, we aim to enable more efficient and scalable FaaS platforms capable of handling diverse workloads with minimal initialization overhead.

Serverless computing promises convenient abstractions for developing and deploying functions that execute in response to events. In such Function-as-a-Service (FaaS) platforms, scheduling is an integral task, but current scheduling algorithms often struggle with maintaining balanced loads, minimizing cold starts, and adapting to commonly occurring bursty workloads. In this work, we propose pull-based scheduling as a novel scheduling algorithm for serverless computing. Our key idea is to decouple worker selection from task assignment, with idle workers requesting new tasks proactively. Experimental evaluation on an open-source FaaS platform shows that pull-based scheduling, compared to other existing scheduling algorithms, significantly improves the performance and load balancing of serverless workloads, especially under high concurrency. The proposed algorithm improves response latencies by 14.9 % compared to hash-based scheduling, reduces the frequency of cold starts from 43 % to 30 %, increases throughput by 8.3 %, and achieves a more even load distribution by 12.9 % measured by the requests assigned per worker.

In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a low-cost framework, which is capable of generating complex, multi-hop questions. We evaluated state-of-the-art VQA models on ReasonVQA, and the empirical results demonstrate that ReasonVQA poses significant challenges to these models, highlighting its potential for benchmarking and advancing the field of VQA. Additionally, our dataset can be easily scaled with respect to input images; the current version surpasses the largest existing datasets requiring external knowledge by more than an order of magnitude.

Cloud benchmarks suffer from performance fluctuations caused by resource contention, network latency, hardware heterogeneity, and other factors along with decisions taken in the benchmark design. In particular, the sampling strategy of benchmark designers can significantly influence benchmark results. Despite this well-known fact, no systematic approach has been devised so far to make sampling results comparable and guide benchmark designers in choosing their sampling strategy for use within benchmarks. To identify systematic problems, we critically review sampling in recent cloud computing research. Our analysis identifies concerning trends: (i) a high prevalence of non-probability sampling, (ii) over-reliance on a single benchmark, and (iii) restricted access to samples. To address these issues and increase transparency in sampling, we propose methodological guidelines for researchers and reviewers. We hope that our work contributes to improving the generalizability, reproducibility, and reliability of research results.

The availability of vast amounts of visual data with diverse and fruitful features is a key factor for developing, verifying, and benchmarking advanced computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited data distribution for very specific fields of interest, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that VisionKG is not only based on metadata but also utilizes a unified data schema and external knowledge bases to integrate, interlink, and align visual datasets. It enhances the enrichment of the semantic descriptions and interpretation at both image and instance levels and offers data retrieval and exploratory services via SPARQL and natural language empowered by Large Language Models (LLMs). VisionKG currently contains 617 million RDF triples that describe approximately 61 million entities, which can be accessed at https://vision.semkg.org and through APIs. With the integration of 37 datasets and four popular computer vision tasks, we demonstrate its usefulness across various scenarios when working with computer vision pipelines.

Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions; however, its performance degrades notably in low-visibility scenarios, especially at night, posing challenges not only for its adaptability in low signal-to-noise ratio (SNR) conditions but also for the reliability and efficiency of automated vehicles. To address this problem, we propose a Cooperative Students (CoS) framework that innovatively employs global-local transformations (GLT) and a proxy-based target consistency (PTC) mechanism to capture the spatial consistency in day- and nighttime scenarios effectively, and thus bridge the significant domain shift across contexts. Building upon this, we further devise an adaptive IoU-informed thresholding (AIT) module to gradually avoid overlooking potential true positives and enrich the latent information in the target domain. Comprehensive experiments show that CoS essentially enhanced UDA performance in low-visibility conditions and surpasses current state-of-the-art techniques, achieving an increase in mAP of 3.0%, 1.9%, and 2.5% on BDD100K, SHIFT, and ACDC datasets, respectively.

Dataset distillation is an increasingly prevalent technique for condensing a large-scale dataset into more compact versions while preserving their intrinsic utility. However, very few studies have investigated the trustworthiness of data distillation, i.e., privacy, robustness, and fairness. The deficiency is particularly striking given the existing research that underscores the vulnerabilities in current AI models, including privacy breaches, biased predictions against underrepresented subgroups, and susceptibility to imperceptible attacks. To bridge the gap, we propose a trustworthy benchmark for assessing representative dataset distillation solutions across the benchmark CIFAR10 with comprehensive evaluation metrics. Through extensive experiments, we uncover vulnerabilities inherent in the application of dataset distillation, offering valuable insights for practitioners. Our work aims to drive the development of more transparent, reliable, and responsible machine learning models, fostering AI systems that align with trustworthy principles.

Climate change has put significant pressure on energy markets. Political decisions such as the plan of the German government to shut down coal power plants by 2038 are shifting electricity production towards renewable and distributed energy resources. The share of these resources will continue to grow significantly in the coming years. This trend changes the ways how energy markets work which mandates fundamental changes in the underlying IT infrastructure. In this paper, we propose a blockchain-based solution which enables an economically viable and grid-serving integration of distributed energy resources into the existing energy system. Our blockchain-based approach targets intraday and day-ahead operating reserve markets, on which energy grid operators and operators of distributed energy resources can trade flexibilities within the schedulable energy production and consumption of their resources. By utilizing these flexibilities as an operating reserve, renewable and climate-friendly technologies can contribute to maintaining the grid stability and security of supply while simultaneously creating economically interesting business models for their operators. We propose to define blockchain-based short-term energy markets by utilizing the concept of general-purpose smart contracts and cryptocurrencies. This enables direct and decentralized trading of energy flexibilities without any intermediary or central instance. We demonstrate the feasibility of our approach through an implementation of a prototype of the proposed markets based on the Ethereum blockchain and provide a detailed evaluation of its efficiency and scalability.

Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate choice of a detection method impacts many properties of mission-critical applications such as in monitoring a patient's health, where anomalies are inevitable but need to be detected securely. In this demo, we present a new evaluator that allows to peruse the performance of several anomaly detection techniques and supports practitioners in understanding the behavior and (dis-)advantages of each technique for a given dataset. In a simple and well-structured way, practitioners can specify the desired anomaly detection setup, and our system would tune the parameters of each technique and analyze their properties in an easily understandable report. The tool also allows recommending the most appropriate technique for each anomaly type and evaluation metric.

The timely, traceable and provenance-aware publication of Linked Open Data (LOD) is crucial for its success and to fulfill the vision of a global, decentralized, and machine-readable database of knowledge. Yet, the access to LOD is still fragmented and mainly centralized aggregations are being used, relying on complex harvesting mechanisms. As a remedy, we propose a blockchain-based approach enabling an integrated, traceable, and timely view on LOD. We use a blockchain to meet the organizational requirements of publishing LOD in a decentralized fashion while still supporting the sovereignty of the data providers and supporting provenance and proper integration into a harmonized knowledge graph. We present an approach and an implemented system that fulfills the requirements regarding volume and throughput and can be used as the foundation for practical deployments. We use Linked Open Government Data (LOGD) as our case study to demonstrate the feasibility of our approach. We developed a prototype to address the specific requirements of LOGD publication and apply the Practical Byzantine Fault Tolerance algorithm at its core to enable a robust state replication.

Cooperative systems have finally entered the automotive mass market with the introduction of the 2020 VW Golf. These "Day-1" functions are aimed at driver information and warning only, but the integration of cooperative systems and automated driver assistance is already planned on several levels such as cooperative perception and cooperative driving maneuvers. The introduction of wireless open networks into highly critical systems sets highest demands to safety and security. In this paper we examine several cybersecurity attack vectors on Day-1 and future cooperative systems by applying the methodology used in functional safety. We evaluate attack difficulty (exposure), severity and controllability for a selection of current and next-gen functions. From this analysis, we derive associated risks and thus give recommendations to researchers and engineers. Finally, we simulate a selection of attacks on a platoon and evaluate function behavior and the possibility of critical system malfunction.

Abstract The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source.

Life without the Internet is no longer possible nor thinkable. Consider the effects of a prolonged Internet outage: In the least impactful way, most of our kids and peers just would no longer be able to interact with their peers. They might severely miss out on the quality of their leisure time activities which increasingly relies on social networks, online games, YouTube, and other online entertainment offers. This may be a nuisance but still is tolerable. More seriously and economically relevant, manufacturing and trade would no longer work as all interactions inside and among companies rely on a working Internet. Indeed, just-in-time ordering mechanisms and Internet of Things-enhanced production chains within the Industry 4.0 framework would no longer be operational as old-style communication means such as phone and faxes have completely been replaced. Indeed, neither of these alternative mechanisms-faxes, phone, and also messaging-would be available either as they also rely on Internet technology. Even worse, the control of critical infrastructures would also be affected severely as they increasingly rely on the Internet for gathering input data and propagating control information. Moreover, all big data analytic applications, including financial transactions, would fail as they can no longer gather and process their input data. Even worse, the fact that there is “no communication without energy” nowadays also means that the reciprocal statement applies that there is no “energy without communication”.

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triplestores. We present methods extending a native RDF store to efficiently handle the storage, tracking, and querying of provenance in RDF data. We describe a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer a query. Subsequently, we present techniques to tailor queries with provenance data. We empirically evaluate the presented methods and show that the overhead of storing and tracking provenance is acceptable. Finally, we show that tailoring a query with provenance information can also significantly improve the performance of query execution.

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project-OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. (www.github.com/OpenIotOrg). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.

Location-based services gained much popularity through providing users with helpful information with respect to their current location. As a recent trend, virtual location-based services consider webpages or sites associated as 'virtual locations' that online users can visit. The presence of links between virtual locations and the corresponding physical locations (e.g., geo-location information of a restaurant linked to its website), allows for novel types of services and applications which constitute virtual location-based services (VLBS). Their success largely depends on the existence of websites referring to physical locations. In this paper, we investigate the usefulness of linking virtual and physical locations. For this, we analyze the presence and distribution of virtual locations, i.e., websites referring to places, for two Irish cities. Using simulated tracks based on a user movement model, we investigate how mobile users move through the Web as virtual space. Our results show that virtual locations are omnipresent in urban areas, and that the situation that a user is close to even several such locations at any time is rather the normal case instead of the exception.

The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.

The management performance of cloud systems is measured by the capacity of the cloud for controlling virtual infrastructures and their capability to run parallel-computing applications and distributed-processing services independently. The challenge about how this management performance can be done more dynamically (self-organization) by means of distributed user data and application data demands is yet an area to explore. This paper introduces first a functional architecture design, following the principles for cloud-based service lifecycle control and service composition in cloud, and second an in-house approach enabling self-organization for cloud services controlling the installation of virtual machines by using event-driven management operations acting as a proof of concept implementation. From a management point of view in cloud, enabling control of virtual infrastructures as a response to performance protocols by means of event(s) data processing is fundamental. Likewise managing cloud services lifecycle by enabling scalable applications and using distributed information systems and linked data processing, guarantee the self-organizing feature for cloud systems. Finally multiple advantages arise when infrastructure performance and end user data are used in cloud service management as it is discussed in this paper.

A set of well-integrated clinical terminologies is at the core of delivering an efficient clinical trial system. The design and outcomes of a clinical trial can be improved significantly through an unambiguous and consistent set of clinical terminologies used in a participating clinical institute. However, due to lack of generalised legal and technical standards, heterogeneity exists between prominent clinical terminologies as well as within and between clinical systems at several levels, e.g., data, schema, and medical codes. This article specifically addresses the problem of integrating local or proprietary clinical terminologies with the globally defined universal concepts or terminologies. To deal with the problem of ambiguous, inconsistent, and overlapping clinical terminologies, domain and knowledge representation specialists have been repeatedly advocated the use of formal ontologies. We address two key challenges in developing an ontology-based clinical terminology (1) an ontology building methodology for clinical terminologies that are separated in global and local layers, and (2) aligning global and local clinical terminologies. We present Semantic Electronic Health Record (SEHR) ontology that covers multiple sub-domains of Healthcare and Life Sciences (HCLS) through specialisation of the upper-level Basic Formal Ontology (BFO). One of the main features of SEHR is layering and adaptation of local clinical terminologies with the upper-level BFO. Our empirical evaluation shows an agreement of clinical experts confirming SEHR's usability in clinical trials.

Despite the services of sophisticated search engines, there are interesting information sources which are useful but largely inaccessible to current web users. These sources are often ad-hoc, location-specific and only useful for users over short periods of time, or relate to tacit knowledge of users or crowds. The solution presented in this paper introduces an integrated concept of "location" and "presence" across the physical and virtual worlds enabling ad-hoc socializing of users looking for similar information. While the definition of presence in the physical world is straightforward their definitions in the virtual world are neither obvious nor trivial. We provide an integrated spatial model spanning both worlds which enables us to define presence of users in a unified way. This integrated model allows us to enable ad-hoc socializing of users browsing the Web with users in the physical world specific to their joint information needs and allows us to unlock the untapped information sources mentioned above. We describe our proof-of-concept implementation and provide an empirical analysis based on real-world experiments.

The investigation of the browsing behavior of users has been a topic of active research since the Web started. However, new online services changed the meaning behind "browsing the Web" and require a fresh look at the problem. Platforms such as YouTube or last. Fm have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Particularly social networks (e.g., Facebook) attracted whole new, particularly less tech-savvy audiences. Advances in mobile technologies made browsing "on-the-move" the norm and changed the user behavior, often being influenced by the user's location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. DOBBS provides a browser add-on which keeps track of users' browsing behavior. In this paper, we outline the motivation behind DOBBS, describe the add-on and dataset, and present some first results to highlight the strengths of DOBBS.

Internet of Things (IoT) is an emerging area that not only requires development of infrastructure but also deployment of new services capable of supporting multiple, scalable (cloud-based) and interoperable (multi-domain) applications. In the race of designing the IoT as part of the Future Internet architecture, academic and ICT's (Information and Communication Technology) industry communities have realized that a common IoT problem to be tackled is the interoperability of the information. In this paper we review recent trends and challenges on interoperability, and discuss how semantic technologies, open service frameworks and information models can support data interoperability in the design of the Future Internet, taking the IoT and Cloud Computing as reference examples of application domains.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

In this paper we present a replication approach which utilizes the availability information of nodes in the network. By performing Box-Jenkins's ARIMA model on the availability information, a prediction model is devised which helps to predict the future availability of the node. Based on this information highly requested data is replicated to nodes which complement each others availability in the network. This improves the overall availability and reduces the overlapping availability and reduces the excess storage for each replica. We have performed our evaluation on Kadamlia network. The Analysis showsour replication algorithm keeps data sustainable in the network. In our evaluation we show that life patterns along with the availability of nodes can be used to improve overall data availability. Our approach reduces over head compared to existing approaches to data availability.

Abstract Given the inherent autonomy, heterogeneity, and continuous evolution of Web services, mismatches usually exist between service protocols. Adapters are typically used to reconcile these mismatches. Before synthesizing an adapter, a service requestor is often willing to assess whether her expected interactions can be conducted or not. The effort of synthesizing an adapter is beneficial only if the result of this assessment is positive. Previous effort analyzing service interactions focused on either (i) compatibility analysis for checking whether interactions can be conducted in a direct manner or (ii) adapter synthesization for reconciling mismatches. In this paper we propose a new kind of adaptability assessment that (i) determines whether two service protocols of a requestor and a provider are adaptable, (ii) computes an adaptation degree, and (iii) identifies conditions that determine when these two service protocols can be adapted. This adaptability assessment provides complementary criteria to the service requestor for selecting a suitable service protocol from a set of functionally equivalent candidates according to her requirements. Copyright © 2010 John Wiley & Sons, Ltd.

Presence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical cont

Two Phase geographic Greedy Forwarding (TPGF) is a pure on-demand geographic greedy forwarding protocol for wireless multimedia sensor networks (WMSNs). Unlike position-based routing protocols, TPGF has explicit route discovery, i.e., a node greedily forwards a routing packet to the neighbor that is the closest one to the destination to build a route. Thus, TPGF is vulnerable to some greedy forwarding attacks, e.g., spoofing or modifying control packets. In this paper, we identify such vulnerabilities and propose corresponding countermeasures for TPGF, e.g., secure neighbor discovery, route discovery.

For high-density networks, several studies have proposed field-based routing paradigms to uniformly distribute the traffic load throughout the network. However, as network density decreases, we observe major shortcomings of the current state-of-the-art: (i) fewer number of neighbors reduce the number of available paths and leads to path merging and (ii) the paths directed towards the border of the network merge into a single path. These path merging effects decrease significantly the energy balance, and as consequence, the lifetime of the network. In this paper, we propose a novel mechanism to enable a better load balancing for single-source and multiple-source scenarios. Our evaluations demonstrate that by using the proposed methodology, the network lifetime can be prolonged between 30% and 40%.

In this paper we present a replication strategy to improve data availability in P2P Networks. The focus of the paper is to replicate data to nodes which are highly available and complement one another in terms of uptimes. This would decrease the replication management overhead until the number of replicas falls to a certain threshold. Replication to reliable node would improve the cost of replication by avoiding irregular nodes of the network. We run Monte Carlo simulation based on past traces of Kad, OverNet, Bittorrent and PlanetLab, to present how our replication keeps data sustainable in the network. In our evaluation we show that a life pattern along with the availability of nodes improves overall data availability. We perform our evaluation on a Kademlia network, and show that our approach reduces over head compared to existing approaches to data availability.

We identify the gap that currently exists between enterprise and consumer-focused mashup tools. We describe how Sqwelch, a semantically-enabled mashup maker, addresses this gap during the design of mashups and in their execution. Sqwelch enables the composition of mashups based on the concept of trust explicitly specified by users through a visual interface. Taxonomies are used to enable lightweight mediation of payloads delivered through a publish/subscribe mechanism. We demonstrate the use of Sqwelch as a proof of concept in the remote delivery of healthcare, and how we have used Sqwelch to address areas of trust and collaboration in the delivery of telehealth services.

This paper proposes a formal technique to verify whether or not an expected interaction is adaptable. We first present our observation that a mediated service interaction is synchronizable. This fact is a prerequisite of our approach. Hereafter, we formally model a protocol scenario (i.e., a part of

Workflow models have been used and refined for years to execute processes within organisations. To deal with collaborative processes (choreographies) these internal workflow models have to be aligned with the external behaviour advertised through Web service interfaces. However, traditional workflow management systems (WfMS) do not offer this functionality. Simply sharing and merging process models is often not possible, because workflow management lacks a widely accepted standard theory for workflow models.Multiple research and standardisation efforts to integrate different workflow theories have been proposed over the years. XPDL is the most widely used standard for process model interchange and supported by over 80 systems.However, XPDL also lacks the possibility to relate a workflow model to its possible choreography interface abstractions.To remedy this situation, we propose to abstract the XPDL model to a higher-level model, perform the integration and the compaction algorithms at that level and then ground it back to the desired choreography models. We develop and use an integrated ontology which is based on the XPDL standard for this purpose. To facilitate the abstraction and grounding, we present a mapping procedure to automatically translate XPDL and BPMN workflow models into this ontology. After translation, these models are annotated with a parameterised role model and other collaborative properties. We present a compaction procedure that automatically maps the annotated models into external choreography interfaces that expose only the relevant information for a particular partner collaboration. Our procedure is agnostic with respect to the target choreography model. We demonstrate our approach using WSMO choreographies which enables us to automatically generate interface models from any WfMSs that supports XPDL export.

We present a semantic optimized service discovery (SemOSD) approach capable of handling Web service search requests on a fine-grained level of detail where we augment semantic service descriptions with statistically built predictor functions. Our approach combines ontologies and mathematical functions built using statistical regression over previous Web service interactions. In the search requests we allow for arbitrary, independent and dependent constraints and user preferences expressed using objective functions. Our approach maps to standard operational research global optimization problem where algorithms of simulated annealing and differential evolution are used. It is capable of finding the optimal combination of service input and output parameters (a configuration) to a user request with rich preferences. Our approach is applied to an international package shipment scenario where real (Web) services are used and mined to create price prediction models. We show that the chosen regression method provides price prediction models of high accuracy and our approach supports expressive and complex search requests.

This work presents a distributed routing protocol to help mobile users locate items in passive wireless environments. In these environments, a mobile user can detect items that are in its proximity, but items can not communicate directly with each other. For example, an area where passive RFID tags are embedded in the environment and mobile users are provided with RFID readers. The localization protocol is based on the following idea: while searching for a specific node, mobile users populate the memory of the nodes they encounter with information about the nodes they have already seen; later, this information is used to guide other users. The contribution of our work is to demonstrate the feasibility of the proposed protocol, in particular, we provide: (a) a lower bound for the required memory space (in the nodes) to store routing information, (b) a proof that disseminate-while-search routing is loop-free and (c) an study on the extent of user mobility required to disseminate the routing information. A proof-of-concept of the proposed protocol was implemented in a small test-bed of MicaZ motes resembling a passive environment.

The use of RDF data published on the Web for applications is still a cumbersome and resource-intensive task due to the limited software support and the lack of standard programming paradigms to deal with everyday problems such as combination of RDF data from dierent sources, object identifier consolidation, ontology alignment and mediation, or plain querying and filtering tasks. In this paper we present a framework, Semantic Web Pipes, that supports fast implementation of Semantic data mash-ups while preserving desirable properties such as abstraction, encapsulation, component-orientation, code re-usability and maintainability which are common and well supported in other application areas.

The existence of an increasing number of different ontologies used in semantic web service fields raises numerous interoperation and communication problems with respect to service discovery, composition, and execution. Current approaches for ontology

Abstract Packet filtering used in query process is an important approach to reduce energy consumption in sensor networks.Interestis used as a constraint to filter uninterested data when users query information. Within interested data some are more important because they have more valuable information than that of others. We hope to process these more important data first. In this paper, we firstly definerewardto denote the importance level of data, and then we present a two tiers buffer model as the platform for our ETRI packet filtering algorithm. We especially present two packet filtering algorithms for real time and non‐real time transmissions. Based on simulation result, we find out that both packet filtering algorithms can substantially improve the quality of information and reduce energy consumption. Copyright © 2007 John Wiley & Sons, Ltd.

Web service interoperations have triggered a growing interest in analyzing compatibility and similarity for public processes. However, current approaches are limited since they mainly focus on control-flow but largely ignore data-flow. This ignorance causes that they improperly regard public processes, which can interact with or be replaced by each other with the help of process mediators, as incompatible or different. To address these problems, we propose a novel approach to check compatibility and similarity of public processes considering both control-flow and data-flow. We firstly generate scenarios and views to describe a public process. Then the degree of compatibility and similarity of public processes are computed based on pairwise compatibility or similarity of their views. Our approach will benefit service modelers and users not only for a better understanding of public processes, but also for checking compatibility and similarity of public processes, which will further identify and solve mismatches, and thus facilitate Web service interoperations.

Structured P2P overlays supporting standard database functionalities are a popular choice for building large-scale distributed data management systems. In such systems, estimating the number of answers for structured queries can help approximating query completeness, but is especially challenging. In this paper, we propose to use routing graphs in order to achieve this. We introduce the general approach and briefly discuss further aspects like overhead and guarantees.

Web service interactions lie in the core of SOA. Due to the autonomy, heterogeneity and continuous evolution of Web services, mediators are usually needed to support service interactions to overcome possible mismatches that may exist among business processes. In this paper, we introduce a space-based architecture for process mediator which considers both control-flow and data-flow, present possible mismatch patterns, and suggest how they can be automatically mediated. Our work can be used to perform runtime mediation and thus to facilitate service interactions.

A crucial prerequisite for the deployment and success of Peer-to-Peer data management applications is the availability of metadata in a way that makes it easy to access and combine data from different sources and domains.

Control and data dependencies are important information in business processes that supports process modeling, analysis, and execution. However, sequencing constraints, which are prescribed by control structures, obfuscate the true sources of dependencies. In addition, most work improperly equalizes sequencing constraint and control dependency, and regards data dependencies as a flow of data processing relying on sequencing constraints.In this paper, business activities are described with a semantic description that defines precondition, effect, input, and output. Based on which we specify what control and data dependencies are. Control dependencies are related to the precondition and the effect. Mandatory data dependencies are related to the input and the output, while optional data dependencies are derived from possible conditions on business activities. All control and data dependencies are optimized into a minimal dependency graph which captures essential dependencies to be preserved. A sequencing constraint is possibly due to control and/or data dependencies. A clear view on the relation and the difference between sequencing constraint and control/data dependency is crucial to better support process modeling, analysis, and execution.

Due to the inherent autonomy and heterogeneity of Web services, ensuring composite services reliability remains a challenging problem. Extending the classical control flow with a transactional flow (encapsulating a set of recovery mechanisms) is widely accepted for ensuring composite services reliability. However, current approaches define recovery mechanisms in and ad-hoc way while they have to respect consistency rules regarding the control flow. In this paper, we propose a formal model based on event calculus for capturing both control and transactional flow of composite services. Following a pattern based modelling approach, we depict a set of rules characterising consistent transactional flow that can be defined w.r.t a given control flow. In addition, we propose two complementary approaches for enhancing composite services reliability.

Although the development of service infrastructure has gone through significant improvements, addressing service dynamism, heterogeneity and distribution, it still lacks a scalable and efficient mechanisms fitting the nature of the Web. In particular, methods to publish and find services based on decentralised approaches of service repositories are still in its infancy. We aim at providing an efficient solution for publishing and finding services dynamically using a transparent repository mechanism facilitating service providers who can join and leave a repository network on-the-fly while keeping the ownership of their own repositories. In this respect, we propose a semantic-overlay network of service repositories by exploiting semantics and peer-to-peer technology for query routing and for grouping repositories having similar information. We allow repositories to be in different semantic groups thereby forming a group of coordinating repository nodes. This way, we allow execution of complex queries with minimal query response times which in turn significantly reduces a number of repositories that need to be queried to get desired results.

The idea of collecting and combining large public data sets and services became more and more popular. The special characteristics of such systems and the requirements of the participants demand for strictly decentralized solutions. However, this comes along with several ambitious challenges a corresponding system has to overcome. In this demonstration paper, we present a lightweight distributed universal storage capable of dealing with those challenges, and providing a powerful and flexible way of building Internet-scale public data management systems. We introduce our approach based on a triple storage on top of a distributed hash table (DHT) overlay system, based on the ideas of a universal relation model and the resource description framework (RDF), and outline solved challenges as well as open issues.

We present an extensible and customizable framework for the autonomous discovery of Semantic Web services based on their QoS properties. Using semantic technologies, users can specify the QoS matching model and customize the ranking of services flexibly according to their preferences. The formal modeling of the discovery process as a query execution plan facilitates the introduction of different discovery algorithms and the automatic generation of parallelized matchmaking evaluations. This enables adapting our approach to unpredictable arrival rates of user queries and scales up to high numbers of published service descriptions.

With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global "sensor Internet." This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zero-programming) effort. This paper describes the global sensor networks (GSN) middleware which addresses these goals. We present GSN's conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://gsn.sourceforge.net/.

Within the vision of ubiquitous multimedia, using multimedia sensor nodes, such as audio, image, and video sensor nodes, in wireless sensor networks can dramatically enhance the capability of sensor networks for event description. Different kinds of holes can easily appear in sensor networks. For example, tens of sensor nodes can consist of a hole in a small area because of overload. Efficiently and reliably transmit these multimedia streaming data back to the base station while bypassing these static or dynamic holes is one of the key challenge issues. In this paper, we propose a new Two Phase geographical Greedy Forwarding (TPGF) routing algorithm for exploring one or multiple optimized hole-bypassing transmission paths. We demonstrate the execution of TPGF routing algorithm by using our implemented simulator NetTopo. Comparison and simulation results show that our algorithm can effectively solve the identified problems and is better than the famous GPSR routing algorithm in both functionality and performance aspects.

Different ontologies used in semantic web services fields raise numerous interoperation and communication problems with respect to service discovery, composition, and execution. The current approaches for ontology mediation often failed due to their lack of sufficient semantic expressiveness and reasoning capability. In this paper1, we present a novel approach allowing ontologies to provide self-contained semantics for service applications. We show how desired application ontologies can be generated using a new merging algorithm for service ontologies. We also show some experimental results and compare them to the output of the PROMPT ontology merging tool.

Large-scale distributed data management with P2P systems requires the existence of similarity operators for queries as we cannot assume that all users agree on exactly the same schema and value representations and data quality problems due to spelling errors and typos. In this paper, we present an approach for efficient processing of similarity selections and joins in a structured overlay. We show that there are several possible strategies exploiting DHT features to a different extent (i.e., key organization, routing, multicasting) and thus the choice of the best operator implementation in a given situation (selectivity, data distribution, load) should be based on cost information allowing the system to estimate the computation and communication costs of query execution plans. Hence, we present a cost model for similarity operations on structured data in a DHT and demonstrate the efficiency of our proposal by experimental results from a large-scale PlanetLab deployment

The success of the P2P idea has created a huge diversity of approaches, among which overlay networks, for example, Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS, have received specific attention from both developers and researchers. A wide variety of algorithms, data structures, and architectures have been proposed. The terminologies and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable.

Among the open problems in P2P systems, support for nontrivial search predicates, standardized query languages, distributed query processing, query load balancing, and quality of query results have been identified as some of the most relevant issues. This paper describes how range queries as an important nontrivial search predicate can be supported in a structured overlay network that provides O(log n) search complexity on top of a trie abstraction. We provide analytical results that show that the proposed approach is efficient, supports arbitrary granularity of ranges, and demonstrate that its algorithmic complexity in terms of messages is independent of the size of the queried ranges and only depends on the size of the result set. In contrast to other systems which provide evaluation results only through simulations, we validate the theoretical analysis of the algorithms with large-scale experiments on the PlanetLab infrastructure using a fully-fledged implementation of our approach.

Identification is an essential building block for many services in distributed information systems. The quality and purpose of identification may differ, but the basic underlying problem is always to bind a set of attributes to an identifier in a unique and deterministic way. Name/directory services, such as DNS, X.500, or UDDI, are a well-established concept to address this problem in distributed information systems. However, none of these services addresses the specific requirements of peer-to-peer systems with respect to dynamism, decentralization, and maintenance. We propose the implementation of directories using a structured peer-to-peer overlay network and apply this approach to support self-contained maintenance of routing tables with dynamic IP addresses in structured P2P systems. Thus, we keep routing tables intact without affecting the organization of the overlay networks, making it logically independent of the underlying network infrastructure. Even though the directory is self-referential, since it uses its own service to maintain itself, we show that it is robust due to a self-healing capability. For security, we apply a combination of PGP-like public key distribution and a quorum-based query scheme. We describe the algorithm as implemented in the P-Grid P2P lookup system (http:// www.p-grid.org/) and give a detailed analysis and simulation results demonstrating the efficiency and robustness of our approach.

This paper studies the problem of updates in decentralised and self-organising P2P systems in which peers have low online probabilities and only local knowledge. The update strategy we propose for this environment is based on a hybrid push/pull rumor spreading algorithm and provides a fully decentralised, efficient and robust communication scheme which offers probabilistic guarantees rather than ensuring strict consistency. We describe a generic analytical model to investigate the utility of our hybrid update propagation scheme from the perspective of communication overhead.

This paper describes a novel approach for obtaining semantic interoperability among data sources in a bottom-up, semi-automatic manner without relying on pre-existing, global semantic models. We assume that large amounts of data exist that have been organized and annotated according to local schemas. Seeing semantics as a form of agreement, our approach enables the participating data sources to incrementally develop global agreement in an evolutionary and completely decentralized process that solely relies on pair-wise, local interactions: Participants provide translations between schemas they are interested in and can learn about other translations by routing queries (gossiping). To support the participants in assessing the semantic quality of the achieved agreements we develop a formal framework that takes into account both syntactic and semantic criteria. The assessment process is incremental and the quality ratings are adjusted along with the operation of the system. Ultimately, this process results in global agreement, i.e., the semantics that all participants understand. We discuss strategies to efficiently find translations and provide results from a case study to justify our claims. Our approach applies to any system which provides a communication infrastructure (existing websites or databases, decentralized systems, P2P systems) and offers the opportunity to study semantic interoperability as a global phenomenon in a network of information sharing parties.

The limitations of client/server systems become evident in an Internet-scale distributed environment. P2P systems offer an alternative to traditional client/server systems: Every node acts both as a client and a server and "pays" its participation by providing access to its computing resources. Systems such as Napster and Gnutella have proven their practical applicability. In this tutorial we position the P2P paradigm in the design space of distributed information systems, present underlying models and concepts, and show the structure, protocols, and algorithms of current systems. Then we elaborate on the novel requirements for P2P algorithms (resource discovery, complexity, and scalability) and present future research areas.

The Java platform facilitates to dynamically load and execute code from remote sources which can threaten the security and integrity of a system and the privacy of its users. To address these problems, Java includes a securityarchitecture which is based on a closed policy model. Although this model is sufficient to specify arbitrary policies, it easily may become cumbersome to use and is not well-suited for administering a consistent security policy for a complete network. The Java Secure Execution Framework (JSEF) overcomes these drawbacks: it introduces higher-level abstractions which enhance the expressiveness of policy rules# it simplifies the maintenance of security configurations# and it provides additional functionality and tools to make administration less error-prone. In JSEF we propose a hybrid policy model which supports additive and subtractivepermissions with a denial-take-precedence rule to resolve conflon --- Security profiles can be expressed in terms of hierarchical groups where a subgroup inherits the policy defined by its parent. All members of a group share the same set of permissions and users can be members of an arbitrary number of groups. JSEF's administrative model supports the definition of a network-wide policy which users can tailor to their needs but not break. At runtime JSEF enforces the defined security policy and supports security negotiation in case of insufficient permissions. A set of graphical tools supports the user in defining security policies and configuring JSEF.