The serverless cloud computing execution model presents an opportunity for implementing data processing pipelines in a cost efficient manner with minimum or no consideration about managing their operational aspects. In doing so, however, a major concern that emerges is the adoption of provider-specific languages required for orchestrating serverless functions and supporting services into the higher order logic of pipelines. As a result, the vendor lock-in problem inherent to cloud computing becomes further intensified. In this work we propose an approach to mitigate this issue through the adoption and adaptation of well-known patterns from the literature as the means for modeling the pipeline logic. By providing mappings from these patterns to vendor-specific orchestration language constructs we allow for efficient realization of provider-agnostic pipeline models as provider-specific executable workflows. An industrial case study provides evidence towards the suitability of our proposal for practical purposes.

Directly mining high-dimensional time series presents several challenges, such as time and space costs. This study proposes a new approach for representing time series data and evaluates its effectiveness in detecting collective anomalies. The proposed method, called bidirectional piecewise linear representation (BPLR), represents the original time series using a set of linear fitting functions, which allows for dimensionality reduction while maintaining its dynamic characteristics. Similarity measurement is then performed using the piecewise integration (PI) approach, which achieves good detection performance with low computational overhead. Experimental results on synthetic and real-world data sets confirm the effectiveness and advantages of the proposed approach. The ability of the proposed method to capture more dynamic details of time series leads to consistently superior performance compared to other existing methods.

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular because of its ability to deliver a large number of computing resources on-demand. Security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target for both business and scientific workflows. The analysis shows that most of the existing literature focuses on the modeling and execution phases, while the monitoring and adaptation phases are not covered adequately by a scarce amount of publications thus leaving a huge gap in the body of knowledge relevant to detection, prevention of and reaction to security violations in cloud-based workflows.

Genome-wide association studies (GWAS) focus on finding associations between genotypes and phenotypes such as susceptibility to diseases. Since genetic data is extremely sensitive and long-lived, individuals and organizations are reluctant to share their data for analysis. This paper proposes two solutions for a fully decentralized and privacy-preserving system for performing minor allele frequency analysis on multiple data sets. Homomorphic encryption and zero-knowledge proofs are used in combination with a blockchain system to achieve data privacy and enable verifiability. Preliminary evaluation of the solutions reveals several important challenges such as handling large cipher texts in smart contracts and reuse of the encrypted data for specific researcher queries that need to be tackled in order to make the solutions more practical.

Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datasets can greatly improve healthcare and pave the way for personalized medicine. Sharing genomic datasets, however, poses major challenges, as genomic data is different from traditional medical data, indirectly revealing information about descendants and relatives of the data owner and carrying valid information even after the owner passes away. Therefore, stringent data ownership and control measures are required when dealing with genomic data. In order to provide a secure and accountable infrastructure, blockchain technologies offer a promising alternative to traditional distributed systems. Indeed, the research on blockchain-based infrastructures tailored to genomics is on the rise. However, there is a lack of a comprehensive literature review that summarizes the current state-of-the-art methods in the applications of blockchain in genomics. In this article, we systematically look at the existing work both commercial and academic, and discuss the major opportunities and challenges. Our study is driven by five research questions that we aim to answer in our review. We also present our projections of future research directions which we hope the researchers interested in the area can benefit from.

A crucial challenge in digital image forensics is to identify the source camera model used to generate given images. This is of prime importance, especially for Law Enforcement Agencies in their investigations of Child Sexual Abuse Material found in darknets or seized storage devices. In this work, we address this challenge by proposing a solution that is characterized by two main contributions. It relies on the extraction of rather small homogeneous regions that we extract very efficiently from the integral image, and on a hierarchical classification approach with convolutional neural networks as the underlying models. We rely on homogeneous regions as they contain camera traces that are less distorted than regions with high-level scene content. The hierarchical approach that we propose is important for scaling up and making minimal modifications when new cameras are added. Furthermore, this scheme performs better than the traditional single classifier approach. By means of thorough experimentation on the publicly available Dresden data set, we achieve an accuracy of 99.01% with 5-fold cross-validation on the ‘natural’ subset of this data set. To the best of our knowledge, this is the best result ever reported for Dresden data set.

One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though the proposed approach is not yet forensics ready, our experiments show that this direction is worth pursuing, achieving at this moment 85 percent accuracy. This ongoing work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.

Scientists are increasingly using the workflow technology as a means for modeling and execution of scientific experiments. Despite being a very powerful paradigm workflows still lack support for trial-and-error modeling, as well as flexibility mechanisms that enable the ad hoc repetition of experiment logic to enable, for example, the convergence of results or to handle errors. In this respect, in our work on enabling multi-scale/field (multi-*) experiments using choreographies of scientific workflows, we contribute a method comprising all necessary steps to conduct the repetition of choreography logic across all workflow instances participating in a multi-* experiment. To realize the method, we contribute i) a formal model representing choreography models and instances, including the re-execute and iterate operations for choreographies, and based on it ii) algorithms for determining the rewinding points, i.e., the activity instances where the rewinding has to stop and iii) enable the actual rewinding to a previous execution state and repetition of the choreography. We present the implementation of our approach in a message-based, service-oriented system that allows scientists to model, control, and execute scientific choreographies as well as perform the rewinding and repeating of choreography logic. We also provide an evaluation of the performance of our approach.

Utility, defined as the perceived satisfaction with a service, provides the ideal means for decision making on the level of individual entities and collectives participating in a large-scale dynamic system.Previous works have already introduced the concept into the area of collective adaptive systems, and have discussed what is the necessary infrastructure to support the realization of the involved theoretical concepts into actual decision making.In this work we focus on two aspects.First, we provide a concrete utility model for a case study that is part of a larger research project.Second, we incorporate this model into our implementation of the proposed architecture.More importantly, we design and execute an experiment that aims to empirically evaluate the use of utility for decision making by comparing it against simpler decision making mechanisms.

This work is motivated by the increasing importance and business value of data in the fields of business process management, scientific workflows as a field in eScience, and Internet of Things, all of which profiting from the recent advances in data science and Big data. We introduce a management life cycle that renders data as first-class citizen in service choreographies and defines the functions and artifacts necessary for enabling transparent and efficient data exchange among choreography participants. The inherent goal of the life cycle, functions and artifacts is to help decouple the data flow, data exchange and management from the control flow in service compositions and choreographies. This decoupling enables peer-to-peer data exchange in choreographies and provides the means for more sophisticated data management and exchange, as well as data exchange and provisioning optimization.

The success of the Cloud computing paradigm, together with the increase of Cloud providers and optimized Infrastructure-as-a-Service (IaaS) offerings have contributed to a raise in the number of research and industry communities that are strong supporters of migrating and running their applications in the Cloud. Focusing on eScience simulation-based applications, scientific workflows have been widely adopted in the last years, and the scientific workflow management systems have become strong candidates for being migrated to the Cloud. In this research work we aim at empirically evaluating multiple Cloud providers and their corresponding optimized and nonoptimized IaaS offerings with respect to their offered performance, and its impact on the incurred monetary costs when migrating and executing a workflow-based simulation environment. The experiments show significant performance improvements and reduced monetary costs when executing the simulation environment in off-premise Clouds.

The on-demand provisioning of services, a cloud-based extension for traditional service-oriented architectures, improves the handling of services in usage scenarios where they are only used rarely and irregularly. However, the standard process of service provisioning and de-provisioning shows still some shortcomings when applying it in real world. In this paper, we introduce a middleware-centric optimization approach that can be integrated in the existing on-demand provisioning middleware in a loosely coupled manner, changing the standard provisioning and de-provisioning behavior in order to improve it with respect to cost and time. We define and implement a set of optimization strategies, evaluate them based on a real world use case from the eScience domain and provide qualitative as well as quantitative decision support for effectively selecting and parametrizing a suitable strategy. Altogether, our work improves the applicability of the existing on-demand provisioning approach and system in real world, including guidance for selecting the suitable optimization strategy for specific use cases.

Reuse of service orchestrations or service compositions is extensively studied in the literature of process modeling. Sub-processes, process templates, process variants, and process reference models are employed as reusable elements for these purposes. The concept of process fragments has been previously introduced in order to capture parts of a process model and store them for later reuse. However, similar efforts on facilitating the reuse of processes that cross the boundaries of organizations expressed as service choreographies are not available yet. In this paper, we introduce the concept of choreography fragments as reusable elements for service choreography modeling. Choreography fragments can be extracted from choreography models, adapted, stored, and later inserted into new models. Based on a formal model for choreography fragments, we define methods and algorithms for the extraction and insertion of fragments from and into service choreographies. We then discuss an experimental and proof-of-concept evaluation of our proposal.

Large-scale systems comprising of multiple heterogeneous entities are directly influenced by the interactions of their participating entities.Such entities, both physical and virtual, attempt to satisfy their objectives by dynamically collaborating with each other, and thus forming collective adaptive systems.These systems are subject to the dynamicity of the entities' objectives, and to changes to the environment.In this work we focus on the latter, i.e. on providing the means for entities in such systems to model, monitor and evaluate their perceived utility by participating in the system.This allows for them to make informed decisions about their interactions with other entities in the system.For this purpose we propose a utility-based approach for decision making, as well as an architecture that allows for the support of this approach.

Service orientation has significantly facilitated the development of complex distributed systems spanning multiple organizations.However, different application areas approach such systems in domain-specific ways, focusing only on particular aspects relevant for their application types.As a result, we observe a very fragmented landscape of service-oriented systems, which does not enable collaboration across organizations.To address this concern, in this work we introduce the notion of Collaborative, Dynamic and Complex (CDC) systems and position them with respect to existing technologies.In addition, we present how CDC systems are modeled and the steps to provision and execute them.Furthermore, we contribute an architecture and prototypical implementation, which we evaluate by means of a case study in a Cloud-enabled context-aware pervasive application.

One of the key strengths of service oriented architectures, the concept of service composition to reuse and combine existing services in order to achieve new and superior functionality, promises similar advantages when applied to resources oriented architectures. The challenge in this context is how to realize service composition in compliance with the constraints defined by the REST architectural style and how to realize it in a way that it can be integrated to and benefit from existing service composition solutions. Existing approaches to REST service composition are mostly bound to the HTTP protocol and often lack a systematic methodology and a mature and standards based realization approach. In our work, we follow a comprehensible methodology by deriving the key requirements for REST service composition directly from the REST constraints and then mapping these requirements to a standard compliant extension of the BPEL composition language. We performed a general requirements analysis for REST service composition, defined a meta model for a corresponding BPEL extension, realized this extension prototypically and validated it based on a real world use case from the eScience domain. Our work provides a general methodology to enable REST service composition as well as a realization approach that enables the combined composition of WSDL and REST services in a mature and robust way.

Service selection is an important concept in service oriented architectures that enables the dynamic binding of services based on functional and non-functional requirements. The introduction of the concept of on-demand provisioned services significantly changes the nature of services and as a consequence the traditional service selection process does not fit anymore. Existing approaches for service selection rely on the always on semantic of services, an assumption that is not valid for on-demand provisioned services. We tackle this problem by adapting the traditional service selection process and by defining an additional step covering the changes introduced by the concept of on-demand provisioning. Our solution comprises an extended architecture for on-demand provisioning, a metamodel for a service registry, and a detailed definition and discussion of the adapted and extended service selection process. The work presented in this paper allows keeping the advantages of dynamic service binding at runtime and combining them with the advantages of Cloud computing exploited through the concept of on-demand provisioning.

Current systems for enacting scientific experiments, and in particular simulation workflows, do not support multi-scale and multi-field problems if they are not coupled on the level of the mathematical model. We present a life cycle that utilizes the notion of choreographies to enable the trial-and-error modeling and execution of multi-scale and/or multi-field simulations. The life cycle exhibits two views reflecting the characteristics of modeling and execution in a top-down and bottom-up manner. It defines techniques for composing data-intensive, scientific workflows in more complex simulations in a generic, domain-independent way, and thus provides scientists with means for collaborative and integrated data management based on the workflow paradigm.

The design of applications that comply to the REST architectural style requires observing a given set of architectural constraints. Following these constraints and therefore designing REST compliant applications is a non-trivial task often not fulfilled properly. There exist several approaches for the modeling and formal description of REST applications, but most of them do not pay any attention to how these approaches can support or even force REST compliance. In this paper we propose a model-driven approach for modeling REST services. We introduce a multi layered model which enables (partially) enforcing REST compliance by separating different concerns through separate models. We contribute a multi layered meta-model for REST applications, discuss the connection to REST compliance and show an implementation of our approach based on the proposed meta-model and method. As a result our approach provides a holistic method for the design and realization of REST applications exhibiting the desired level of compliance to the constraints of the REST architectural style.

Migrating an existing application to the Cloud is a complex and multi-dimensional problem requiring in many cases adapting the application in significant ways. Looking specifically into the database layer of the application, i.e. the aspect providing data persistence and manipulation capabilities, this involves dealing with differences in the granularity of interactions, refactoring of the application to cope with remote data sources, and addressing data confidentiality concerns. Toward this goal, in this work we present an application migration methodology which incorporates these aspects, and a decision support, application refactoring and data migration tool that assists application developers in realizing this methodology. For purposes of evaluating our proposal we present the results of a case study conducted in the context of an eScience project.

The use of information technology in research and practice leads to increased degree of automation of tasks and makes scientific experiments more efficient in terms of cost, speed, accuracy, and flexibility. Scientific workflows have proven useful for the automation of scientific computations. However, not all tasks of an experiment can be automated. Some decisions still need to be made by human users, for instance, how an automated system should proceed in an exceptional situation. To address the need for integration of human users in such automated systems, we propose the concept of Human Communication Flows, which specify best practices about how a scientific workflow can interact with a human user. We developed a human communication framework that implements Communication Flows in a pipes-and-filters architecture and supports both notifications and request-response interactions. Different Communication Services can be plugged into the framework to account for different communication capabilities of human users. We facilitate the use of Communication Flows within a scientific workflow by means of reusable workflow fragments implementing the interaction with the framework.

Simulations are characterized by long running calculations and complex data handling tasks accompanied by non-trivial data dependencies. The workflow technology helps to automate and steer such simulations. Quality of Data frameworks are used to determine the goodness of simulation data, e.g., they analyze the accuracy of input data with regards to the usability within numerical solvers. In this paper, we present generic approaches using evaluated Quality of Data to steer simulation workflows. This allows for ensuring that the predefined requirements such as a precise final result or a short execution time will be met even after the execution of simulation workflow has been started. We discuss mechanisms for steering a simulation on all relevant levels - workflow, service, algorithms, and define a unifying approach to control such workflows. To realize Quality of Data-driven workflows, we present an architecture realizing the presented approach and a WS-Policy-based language to describe Quality of Data requirements and capabilities.

In recent years scientific workflows have been used for conducting data-intensive and long running simulations. Such simulation workflows have processed and produced different types of data whose quality has a strong influence on the final outcome of simulations. Therefore being able to monitor and analyze quality of this data during workflow execution is of paramount importance, as detection of quality problems will enable us to control the execution of simulations efficiently. Unfortunately, existing scientific workflow execution systems do not support the monitoring and analysis of quality of data for multi-scale or multi-domain simulations. In this paper, we examine how quality of data can be comprehensively measured within workflows and how the measured quality can be used to control and adapt running workflows. We present a quality of data measurement process and describe a quality of data monitoring and analysis framework that integrates this measurement process into a workflow management system.

Service-oriented systems have attracted great interest from industry and research communities worldwide. Service integrators, developers, and providers are collaborating to address the various challenges in the field. PESOS 2011 is a forum for all these communities to present and discuss a wide range of topics related to service-oriented systems. The goal of PESOS is to bring together researchers from academia and industry, as well as practitioners working in the areas of software engineering and service-oriented systems to discuss research challenges, recent developments, novel applications, as well as methods, techniques, experiences, and tools to support the engineering of service-oriented systems.

Due to their different target applications business and scientific workflow systems provide different sets of features to their users. Significant amount of research is currently being done to employ the business workflow technology in the scientific domain. This usually means extending the workflow language and thus the modeling tool and execution engine. In this paper we aim to bring business and scientific workflows together in order to exploit the advantages of both. We explore the interplay between business and scientific workflows in the context of human interactions with the management of workflow execution. We present an approach and implementation based on BPEL and Pegasus and show that the approach can be beneficial to scientists.

The performance of applications is influenced by the way its operations are grouped into global transactions. This in turn influences the performance of business processes which utilize these applications as implementations of process activities/steps. Stratified transactions, as produced by the stratification approach presented in this paper, is a way to manage a global transaction by combining the more elemental transactions coordinated using the two-phase commit protocol and queued transactions. The stratification approach can be applied for optimally fragmenting workflow-based service compositions and support the out- and in-sourcing scenarios. This paper formally models global transactions and investigates the mechanisms for building an optimally stratified transaction relying on formally defined evaluation criteria. We investigate the applicability of local search algorithms to the optimization of transaction stratification. In particular we consider hill-climbing, simulated annealing, and a novel hybrid method combining both approaches.

The need for flexibility in process-based applications, in particular during their execution, places the demand for enabling adaptability of processes. AOP is considered to be one of the approaches to flexibly switch on and off functionality on per-instance basis in applications during their execution; analogously, this paradigm can be applied in a BPEL environment to enable adaptation of running orchestrations. In the presented approach we strive towards reuse of as much concepts and technology already available in a Web service (WS) environment as possible. We combine standard BPEL, the publish/subscribe paradigm and WS-Policy so that WS operations play the role of aspects with respect to BPEL processes. We present the syntax for such aspects as an extension of the WS-Policy framework. We introduce the architecture of the supporting infrastructure and a prototypical implementation. The approach draws on the combined benefits of service orientation and the AOP paradigm to improve the state-of-the-art techniques for flexibility of service orchestrations in a non-intrusive manner.

A fundamental principle of service oriented architectures is the decoupling of service requesters and service providers to enable late binding of services at deployment time or even dynamic binding of services at runtime.This is important in enterprise settings, where different services that implement business functions in critical business processes are dynamically chosen based on availability or price. The same problem also applies to dynamic Grid environments where resources need to be dynamically chosen based on availability and other non-functional properties. The WS-Policy framework describes how policies for both providers and requesters are specified to allow the selection of services based on these policies. Existing approaches, using WS-Policy,have drawbacks by placing the burden of the service selection partially on the client. In this paper we present an extended enterprise service bus that allows service clients to submit policies to which service providers need to comply with together in one message with the service invocation request. We show how these policies are evaluated in the bus and how policies are defined for not only stateless services, but also stateful resources.

The existing Web service flow (WS-flow) technologies enable both static and dynamic binding of participating Web services (WSs) on the process model level. Adaptability on per-instance basis is not sufficiently supported and therefore must be addressed to improve process flexibility upon changes in the environment. Ad-hoc process instance changes can be enabled by swapping participating WS instances, by modifying port types of the partners to be invoked, and by changing process logic. In this work, we address the problem of dynamic binding of WSs to WS-flow instances at run time, i.e. the ability to exchange a WS instance participating in a WS-flow instance with an alternative one. The problem is additionally complicated by the fact that the execution of a process depends on its deployment. We describe the "find and bind" mechanism, and we show its representation as a BPEL extension. We discuss the benefits that could be gained and the disadvantages it brings in. The mechanism extends and improves the existing process technologies. It facilitates a precisely controlled policy-based selection of WSs at run time and also provides for process instance repair, while maintaining simplicity. We also discuss a prototypical implementation of the presented functionality.