Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand how their models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.

Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform intelligent tasks without human intervention. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for the AI system users is to comprehend and trust the result of AI algorithms and methods. To address this challenge, we first study the recent techniques in the area of eXplainable Artificial Intelligence (XAI). Then, we introduce a novel XAI process to facilitate producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist the users in comprehending and trusting the results and outputs generated by AI-enabled algorithms, resulting in developing a digital dashboard to facilitate inter-acting with the algorithm. Lastly, we discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms with an application in the banking domain for analyzing customer transactions.

International audience

International audience

This paper explores and offers guidance on a specific and relevant problem in task design for crowdsourcing: how to formulate a complex question used to classify a set of items. In micro-task markets, classification is still among the most popular tasks. We situate our work in the context of information retrieval and multi-predicate classification, i.e., classifying a set of items based on a set of conditions. Our experiments cover a wide range of tasks and domains, and also consider crowd workers alone and in tandem with machine learning classifiers. We provide empirical evidence into how the resulting classification performance is affected by different predicate formulation strategies, emphasizing the importance of predicate formulation as a task design dimension in crowdsourcing.

In this demonstration paper we showcase an extensible and reusable pipeline for automatic paraphrase generation , i.e., reformulating sentences using different words. Capturing the nuances of human language is fundamental to the effectiveness of Conversational AI systems, as it allows them to deal with the different ways users can utter their requests in natural language. Traditional approaches to utterance paraphrasing acquisition, such as hiring experts or crowd-sourcing, involve processes that are often costly or time consuming, and with their own trade-offs in terms of quality. Automatic paraphrasing is emerging as an attractive alternative that promises a fast, scalable and cost-effective process. In this paper we showcase how our extensible and reusable pipeline for automated utterance paraphrasing can support the development of Conversational AI systems by integrating and extending existing techniques under an unified and configurable framework.

Adversarial examples can be detrimental to a recommender,leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance, e.g. raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items, etc. Most existing adversarial example generation methods fall within three categories: attacking the user-item interactions or auxiliary contents, adding perturbations in latent space, sampling the latent space according to certain distribution. In this work, we focus on the semantic-rich user-item interactions in a recommender system and propose a novel generative adversarial network (GAN) named Convolutional Generative Collaborative Filtering (Conv-GCF). We develop an effective perturbation mechanism (adversarial noise layer) for convolutional neural networks (CNN), based on which we design a generator with residual blocks to synthesize user-item interactions. We empirically demonstrate that on Conv-GCF, the adversarial noise layer is superior to the conventional noise-adding approach. Moreover, we propose two types of discriminators: one using Bayes Personalized Ranking (BPR) and the other with binary classification. On four public datasets, we show that our approach achieves the state-of-the-art top-n recommendation performance among competitive baselines.

Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.

Application Programming Interface (API) is a core technology that facilitates developers' productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers that impact a wide range of software development activities. This paper presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO), a widely used programming, community question-answering (CQA) site. Our work proposes an integrated API Knowledge Base (KB) and indexing technique that combines both SO API-related posts as well as other API learning resources collected from the Web (e.g., API video-tutorials from Youtube). The resulting indexed and enriched API community knowledge can be queried in a API-topic-issue driven manner using a simple yet powerful domain-specific language (DSL). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.

This paper reports on the challenges and lessons we learned while running controlled experiments in crowdsourcing platforms. Crowdsourcing is becoming an attractive technique to engage a diverse and large pool of subjects in experimental research, allowing researchers to achieve levels of scale and completion times that would otherwise not be feasible in lab settings. However, the scale and flexibility comes at the cost of multiple and sometimes unknown sources of bias and confounding factors that arise from technical limitations of crowdsourcing platforms and from the challenges of running controlled experiments in the “wild”. In this paper, we take our experience in running systematic evaluations of task design as a motivating example to explore, describe, and quantify the potential impact of running uncontrolled crowdsourcing experiments and derive possible coping strategies. Among the challenges identified, we can mention sampling bias, controlling the assignment of subjects to experimental conditions, learning effects, and reliability of crowdsourcing results. According to our empirical studies, the impact of potential biases and confounding factors can amount to a 38% loss in the utility of the data collected in uncontrolled settings; and it can significantly change the outcome of experiments. These issues ultimately inspired us to implement CrowdHub, a system that sits on top of major crowdsourcing platforms and allows researchers and practitioners to run controlled crowdsourcing projects.

Abstract Rule-based systems have been used increasingly to augment learning algorithms for annotating data. Rules alleviate many of the shortcomings inherent in pure algorithmic approaches, in cases algorithms are not working well or lack from enough training data. However, in dynamic curation environments where data are constantly changing, there is a need to craft and adapt rules to keep them applicable and precise. Rule adaptation has been proven to be painstakingly difficult and error-prone, as an analyst is needed for examining the precision of rules and applying different modifications to adapt the imprecise ones. In this paper, we present an autonomic and conceptual approach to adapt data annotation rules. Our approach offloads analysts from adapting rules; it boosts rules to annotate a larger number of items using a set of high-level conceptual features, e.g. topic. We utilize a Bayesian multi-armed-bandit algorithm, an online learning algorithm that adapts rules based on the feedback collects from the curation environment over time. We propose a summarization technique, which offers a set of high-level conceptual features for annotating items by identifying the semantical relationships among them. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts for adapting rules. The experimental results show that our approach has a comparative performance to analysts in adapting rules.

Text classification is one of the most common goals of machine learning (ML) projects, and also one of the most frequent human intelligence tasks in crowdsourcing platforms. ML has mixed success in such tasks depending on the nature of the problem, while crowd-based classification has proven to be surprisingly effective, but can be expensive. Recently, hybrid text classification algorithms, combining human computation and machine learning, have been proposed to improve accuracy and reduce costs. One way to do so is to have ML highlight or emphasize portions of text that it believes to be more relevant to the decision. Humans can then rely only on this text or read the entire text if the highlighted information is insufficient. In this paper, we investigate if and under what conditions highlighting selected parts of the text can (or cannot) improve classification cost and/or accuracy, and in general how it affects the process and outcome of the human intelligence tasks. We study this through a series of crowdsourcing experiments running over different datasets and with task designs imposing different cognitive demands. Our findings suggest that highlighting is effective in reducing classification effort but does not improve accuracy - and in fact, low-quality highlighting can decrease it.

Most of recent neural network(NN)-based recommendation techniques mainly focus on improving the overall performance, such as hit ratio for top-N recommendation, where the users' feedbacks are considered as the ground-truth. In real-world applications, those feedbacks are possibly contaminated by imperfect user behaviours, posing challenges on the design of robust recommendation methods. Some methods apply man-made noises on the input data to train the networks more effectively (e.g. the collaborative denoising auto-encoder). In this work, we propose a general adversarial training framework for NN-based recommendation models, improving both the model robustness and the overall performance. We apply our approach on the collaborative auto-encoder model, and show that the combination of adversarial training and NN-based models outperforms highly competitive state-of-the-art recommendation methods on three public datasets.

Systematic literature reviews (SLRs) are one of the most common and useful form of scientific research and publication. Tens of thousands of SLRs are published each year, and this rate is growing across all fields of science. Performing an accurate, complete and unbiased SLR is however a difficult and expensive endeavor. This is true in general for all phases of a literature review, and in particular for the paper screening phase, where authors filter a set of potentially in-scope papers based on a number of exclusion criteria. To address the problem, in recent years the research community has began to explore the use of the crowd to allow for a faster, accurate, cheaper and unbiased screening of papers. Initial results show that crowdsourcing can be effective, even for relatively complex reviews.

With Data Science continuing to emerge as a powerful differentiator across industries, organisations are now focused on transforming their data into actionable insights. This task is challenging as in today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of raw data from a variety of sources. Data Lakes introduced as a storage repository to organize this raw data in its native format (supporting from relational to NoSQL DBs) until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to cook/curate them later. In this paper, we present the notion of Knowledge Lake, i.e. a contextualized Data Lake. The Knowledge Lake will provide the foundation for big data analytics by automatically curating the raw data in the Data Lake and to prepare them for deriving insights. We present CoreKG-an open source Data and Knowledge Lake service- which offers researchers and developers a single REST API to organize, curate, index and query their data and metadata in the Lake and over time. CoreKG manages multiple database technologies (from Relational to NoSQL) and offers a built-in design for data curation, security and provenance.

The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from sensors, social-media, news, user-generated, government and private data sources. Accordingly, in a modern data-oriented landscape, with the advent of various data capture and management technologies, organizations are rapidly shifting to datafication of their processes. In such an environment, analysts may need to deal with a collection of datasets, from relational to NoSQL, that holds a vast amount of data gathered from various private/open data islands, i.e. Data Lake. Organizing, indexing and querying the growing volume of internal data and metadata, in a data lake, is challenging and requires various skills and experiences to deal with dozens of new databases and indexing technologies: How to store information items? What technology to use for persisting the data? How to deal with the large volume of streaming data? How to trace and persist information about data? What technology to use for indexing the data? How to query the data lake? To address the above mentioned challenges, we present CoreDB - an open source data lake service - which offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and offers a built-in design for security and tracing.

Literature reviews allow scientists to stand on the shoulders of giants, showing promising directions, summarizing progress, and pointing out existing challenges in research. At the same time conducting a systematic literature review is a laborious and consequently expensive process. In the last decade, there have been several studies on crowdsourcing in literature reviews. This paper explores the feasibility of crowdsourcing for facilitating the literature review process in terms of results, time and effort, and identifies which crowdsourcing strategies provide the best results based on the budget available. In particular we focus on the screening phase of the literature review process and we contribute and assess strategies for running crowdsourcing tasks that are efficient in terms of budget and classification error. Finally, we present our findings based on experiments run on Crowdflower.

Data veracity is a grand challenge for various tasks on the Web. Since the web data sources are inherently unreliable and may provide conflicting information about the same real-world entities, truth discovery is emerging as a countermeasure of resolving the conflicts by discovering the truth, which conforms to the reality, from the multi-source data. A major challenge related to truth discovery is that different data items may have varying numbers of true values (or multi-truth), which counters the assumption of existing truth discovery methods that each data item should have exactly one true value. In this paper, we address this challenge by exploiting and leveraging the implications from multi-source data. In particular, we exploit three types of implications, namely the implicit negative claims, the distribution of positive/negative claims, and the co-occurrence of values in sources' claims, to facilitate multi-truth discovery. We propose a probabilistic approach with improvement measures that incorporate the three implications in all stages of truth discovery process. In particular, incorporating the negative claims enables multi-truth discovery, considering the distribution of positive/negative claims relieves truth discovery from the impact of sources' behavioral features in the specific datasets, and considering values' co-occurrence relationship compensates the information lost from evaluating each value in the same claims individually. Experimental results on three real-world datasets demonstrate the effectiveness of our approach.

Truth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items. Since sources' reliability is unknown a priori, a truth discovery method usually estimates sources' reliability along with the truth discovery process. A major limitation of existing truth discovery methods is that they commonly assume exactly one true value on each data item and therefore cannot deal with the more general case that a data item may have multiple true values (or multi-truth). Since the number of true values may vary from data item to data item, this requires truth discovery methods being able to detect varying numbers of truth values from the multi-source data. In this paper, we propose a multi-truth discovery approach, which addresses the above challenges by providing a generic framework for enhancing existing truth discovery methods. In particular, we redeem the numbers of true values as an important clue for facilitating multi-truth discovery. We present the procedure and components of our approach, and propose three models, namely the byproduct model, the joint model, and the synthesis model to implement our approach. We further propose two extensions to enhance our approach, by leveraging the implications of similar numerical values and values' co-occurrence information in sources' claims to improve the truth discovery accuracy. Experimental studies on real-world datasets demonstrate the effectiveness of our approach.

A large volume of Open Data is being generated on a continuous basis. Examples of this are the case of social, natural, and information systems such as World Wide Web and social networks. Most entities and objects in the Open Data are interconnected, forming a complex, semi-structured, and information-rich networks. In this sense, Linked Open Data has the potential to be similar to a federated database. Since Linked Open Data is based on W3C standards, it is possible to implement a federation infrastructure, however, the current SPARQL standard makes it challenging to analyze the Open Data in an explorative manner. Consequently, it will be hard to discover the hidden knowledge in the relationships among entities in Open Data sources. In this paper, we present Galaxy, a platform for explorative analysis of Open Data Sources. Galaxy facilitates the analysis of Open Data graphs based on simple abstractions, i.e. folders and paths, which enable an analyst to group related entities in the graph or nd paths among entities. Galaxy uses Hadoop data processing platforms to store and retrieve large numbers of RDF triples and to support cost-eective and Web-scale processing of Semantic Web data through a Folder-Path enabled extension of SPARQL.

In many cases, it is not cost effective to automate business processes which affect a small number of people and/or change frequently. We present a novel approach for enabling domain experts to model and deploy such processes from their respective domain as Web service compositions. The approach builds on user-editable service, naming and representing Web services as forms. On this basis, the approach provides a visual composition language with a targeted restriction of control-flow expressivity, process simulation, automated process verification mechanisms, and code generation for executing orchestrations. A Web-based service composition prototype implements this approach, including a WS-BPEL code generator. A small lab user study with 14 participants showed promising results for the usability of the system, even for nontechnical domain experts.

Spreadsheets are used by millions of users as a routine all-purpose data management tool. It is now increasingly necessary for external applications and services to consume spreadsheet data. In this paper, we investigate the problem of transforming spreadsheet data to structured formats required by these applications and services. Unlike prior methods, we propose a novel approach in which transformation logic is embedded into a familiar and expressive spreadsheet-like formula mapping language. Popular transformation patterns provided by transformation languages and mapping tools, that are relevant to spreadsheet-based data transformation, are supported in the language via formulas. Consequently, the language avoids cluttering the source spreadsheets with transformations and turns out to be helpful when multiple schemas are targeted. We implemented a prototype and evaluated the benefits of our approach via experiments in a real application. The experimental results confirmed the benefits of our approach.

With the rapid growth in the number of online Web services, the problem of service adaptation has received significant attention. In matching and adaptation, the functional description of services including interface and data as well as behavioral descriptions are important. Existing work on matching and adaptation focuses only on one aspect.

In this paper we present FormSys, a Web-based system that service-enables form documents. It offers two main services: filling in forms based on Web services' incoming SOAP messages, and invoking Web services based on filled-in forms. This can be applied to benefit individuals to reduce the number of often repetitive form fields they have to complete manually in many scenarios. It can also help organisations to remove the need for manual data entry by automatically triggering business process implementations based on incoming case data from filled-in forms. While the concept applies to forms of any type of document, our implementation uses Adobe AcroForms due to its universal applicability, availability of a usable API, and end-user appeal. In the demo, we will show the two core functions, namely soap2pdf and pdf2soap, along with use case applications of the services developed from real world scenarios. Essentially, this work demonstrates how PDFs can be used as a channel for interacting with Web services.

In this paper, we present the concept of Time Sequence Summarization to support chronology-dependent applications on massive data sources. Time sequence summarization takes as input a time sequence of events that are chronologically ordered. Each event is described by a set of descriptors. Time sequence summarization produces a concise time sequence that can be substituted for the original time sequence in chronology-dependent applications. We propose an algorithm that achieves time sequence summarization based on a generalization, grouping and concept formation process. Generalization expresses event descriptors at higher levels of abstraction using taxonomies while grouping gathers similar events. Concept formation is responsible for reducing the size of the input time sequence of events by representing each group created by one concept. The process is performed in a way such that the overall chronology of events is preserved. The algorithm computes the summary incrementally and has reduced algorithmic complexity. The resulting output is a concise representation, yet, informative enough to directly support chronology-dependent applications. We validate our approach by summarizing one year of financial news provided by Reuters.

Duplicate records are a major problem and duplicate invoices are a specific example of this. The detection of duplicate invoices is a critical issue for business since duplicate invoices can result in a company paying more than once for goods or services ordered. Past experience has shown that generic duplicate record detection techniques are not very useful when applied to invoices: the rate of false positives can be so high that invoice clerks are discouraged from using the system. This is because such approaches do not take the business context into account, e.g. what types of good were ordered as well as the past relationship with that vendor. In this paper, we discuss applying Ripple Down Rules (RDR), an approach for incremental and end-user-centred knowledge acquisition, to the problem of classifying pairs of potential duplicate invoices. We describe how we built a prototype on top of the SAP ERP product and evaluated it on a real data set that had been previously independently audited for duplicates. The preliminary results have highlighted the significant potential of this approach for assisting invoicing clerks processing potential duplicate invoices. We have observed a drop in the rate of false positives from 92% down to 18.66% when compared to traditional approaches that do not take the business context into account. We suggest that incremental development of domain specific knowledge may have more general application to the problem of handling duplicate records.

The rapid growth of social networking sites and web communities have motivated web sites to expose their APIs to external developers who create mashups by assembling existing functionalities. Current APIs, however, aim toward developers with programming expertise; they are not directly usable by wider class of users who do not have programming background, but would nevertheless like to build their own mashups. To address this need, we propose a spreadsheet-based Web mashups development framework, which enables users to develop mashups in the popular spreadsheet environment. First, we provide a mechanism that makes structured data first class values of spreadsheet cells. Second, we propose a new component model that can be used to develop fairly sophisticated mashups, involving joining data sources and keeping spreadsheet data up to date. Third, to simplify mashup development, we provide a collection of spreadsheet-based mashup patterns that captures common Web data access and spreadsheet presentation functionalities. Users can reuse and customize these patterns to build spreadsheet-based Web mashups instead of developing them from scratch. Fourth, we enable users to manipulate structured data presented on spreadsheet in a drag-and-drop fashion. Finally, we have developed and tested a proof-of-concept prototype to demonstrate the utility of the proposed framework.

Major media companies such as The Financial Times, the Wall Street Journal or Reuters generate huge amounts of textual news data on a daily basis. Mining frequent patterns in this mass of information is critical for knowledge workers such as financial analysts, stock traders or economists. Using existing frequent pattern mining (FPM) algorithms for the analysis of news data is difficult because of the size and lack of structuring of the free text news content. In this article, we demonstrate a comprehensive Streaming TEmporAl Data (STEAD) analysis framework for mining frequent patterns in financial news. In this demonstration, we show how the mining task is supported by the use of a Time-Aware Content Summarization algorithm (TACS). This summary generates a concise representation of large volume of data by taking into account the expert's peculiar interest while preserving the news arrival temporal information which is essential for FPM algorithms. We experimented the whole framework on a set of news data from Reuters.

End-user programmers---the 45 million of them, as estimated for 2001 in US alone [7]---routinely use spreadsheet to visualize, manipulate, and analyze data. Thanks to this environment, they can build applications that solve their daily problems. Even building a report can be seen as programming an application that takes corporate data as input and outputs a presentation. To build this application, spreadsheet users have to import data and place them in spreadsheet cells, highlight the important pieces, compute maybe some aggregates, add a chart or two. If well done, this application will be used each time data are updated to effortlessly produce a fresh report.

Business processes (BPs) are central to the operation of both public and private organizations. A business process is a set of coordinated tasks and activities to achieve a business objective or goal. Given the importance of BPs to overall efficiency and effectiveness, the competitiveness of organizations hinges on continuous BP improvement. In the nineties, the focus of BP improvement was on automation: workflow management systems (WfMSs) and other middleware technologies were used to reduce cost and improve efficiency by providing better system integration and automated enactment of operational business processes. Recently, the focus of business process has expanded to monitoring, analysis and understanding of business processes, and such techniques are incorporated in business process management systems (BPMSs).

Understanding the business (interaction) protocol supported by a service is very important for both clients and service providers: it allows developers to know how to write clients that interact with a service, and it allows development tools and runtime middleware to deliver functionality that simplifies the service development lifecycle. It also greatly facilitates the monitoring, visualization, and aggregation of interaction data. This paper presents an approach for discovering protocol definitions from real-world service interaction logs. It first describes the challenges in protocol discovery in such a context. Then, it presents a novel discovery algorithm, which is widely applicable, robust to different kinds of imperfections often present in realworld service logs, and able to derive protocols of small sizes, also thanks to heuristics. As finding the most precise and the smallest model is algorithmically not feasible from imperfect service logs, finally, the paper presents an approach to refine the discovered protocol via user interaction, to compensate for possible imprecision introduced in the discovered model. The approach has been implemented and experimental results show its viability on both synthetic and real-world datasets.

In service-oriented architectures, everything is a service and everyone is a service provider. Web services (or simply services) are loosely coupled software components that are published, discovered, and invoked across the Web. As the use of Web service grows, in order to correctly interact with them, it is important to understand the business protocols that provide clients with the information on how to interact with services. In dynamic Web service environments, service providers need to constantly adapt their business protocols for reflecting the restrictions and requirements proposed by new applications, new business strategies, and new laws, or for fixing problems found in the protocol definition. However, the effective management of such a protocol evolution raises critical problems: one of the most critical issues is how to handle instances running under the old protocol when it has been changed. Simple solutions, such as aborting them or allowing them to continue to run according to the old protocol, can be considered, but they are inapplicable for many reasons (for example, the loss of work already done and the critical nature of work). In this article, we present a framework that supports service managers in managing the business protocol evolution by providing several features, such as a variety of protocol change impact analyses automatically determining which ongoing instances can be migrated to the new version of protocol, and data mining techniques inferring interaction patterns used for classifying ongoing instances migrateable to the new protocol. To support the protocol evolution process, we have also developed database-backed GUI tools on top of our existing system. The proposed approach and tools can help service managers in managing the evolution of ongoing instances when the business protocols of services with which they are interacting have changed.

This paper deals with the problem of discovering protocol models by analyzing real-world interaction logs. There are several scenarios where protocol discovery is useful and needed: (i) In practice, the protocol definition may not be available. This can happen for many reasons, e.g., the service has been developed using a bottom-up approach, by simply SOAP-ifying an existing application; (ii) even when the protocol model is available, protocol discovery is important as we may want to verify if the designed protocol model is what is actually being supported by the implementation and, if not, what are the differences. An instance of this problem involves discovering if the service is compliant with the protocol specification required by some domain-specific standardization body or industry consortium.

In service-oriented computing, a conversation is a sequence of message exchanges between two or more services to achieve a certain goal, for example to order and pay for goods. A business protocol of a service is a specification of the possible conversations that a service can have with its partners. Motivated by the goal of facilitating the scalable development and maintenance of service oriented applications, especially in light of the many benefits of protocols, we have developed ServiceMosaic (servicemosaic.isima.fr), a platform for Web services life-cycle management. ServiceMosaic is an interactive and model-driven CASE tool for managing Web service interactions, which consists of two broad modules: protocol discovery and protocol management.

In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical approach to enable interoperation in such a heterogeneous setting is through developing adapters. There have been approaches for classifying possible mismatches between service interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given two service specifications, the actual mismatches between their interfaces and business protocols. In this paper we present novel techniques and a tool that provides semi-automated support for identifying and resolution of mismatches between service interfaces and protocols, and for generating adapter specification. We make the following main contributions: (i) we identify mismatches between service interfaces, which leads to finding mismatches of type of signature, merge/split, and extra/missing messages; (ii) we identify all ordering mismatches between service protocols and generate a tree, called mismatch tree, for mismatches that require developers' input for their resolution. In addition, we provide semi-automated support in analyzing the mismatch tree to help in resolving such mismatches. We have implemented the approach in a tool inside IBM WID (WebSphere Integration Developer). Our experiments with some real-world case studies show the viability of the proposed approach. The methods and tool are significant in that they considerably simplify the problem of adapting services so that interoperation is possible.

The development of user interfaces (UIs) is one of the most time-consuming aspects in software development. In this context, the lack of proper reuse mechanisms for UIs is increasingly becoming manifest, especially as software development is more and more moving toward composite applications. In this paper we propose a framework for the integration of stand-alone modules or applications, where integration occurs at the presentation layer. Hence, the final goal is to reduce the effort required for UI development by maximizing reus.

There is an increasing demand in making web user interfaces richer and more interactive. Currently, there are two approaches aiming at improving web user interfaces. First, downloaded code in form of Java Applet or ActiveX can be executed in browsers. And more recently, AJAX (Asynchronous JavaScript + XML) leverages browsers' JavaScript engine to render user interfaces without reloading pages. Both approaches have some weaknesses.In this paper, we present an alternative approach to creating highly interactive web user interfaces. Our approach is based on the Extensible User Interface Protocol (XUP), a SOAP-based protocol for communicating events and incremental user interface updates on the web. On top of XUP, we have built a web user interface development framework, OpenXUP, consisting of a thin client and a server toolkit which offers a set of event-driven APIs. The framework allows for the rapid development of highly interactive web applications and services.

More and more suppliers are offering access to their product or information portals (also called e-catalogs) via the Web. The key issue is how to efficiently integrate and query large, intricate, heterogeneous information sources such as e-catalogs. Traditional data integration approach, where the development of an integrated schema requires the understanding of both structure and semantics of all schemas of sources to be integrated, is hardly applicable because of the dynamic nature and size of the Web. We present WS-CatalogNet: a Web services based data sharing middleware infrastructure whose aims is to enhance the potential of e-catalogs by focusing on scalability and flexible aspects of their sharing and access.

A scalable approach to trust negotiation is required in Web service environments that have large and dynamic requester populations. We introduce Trust-Serv, a model-driven trust negotiation framework for Web services. The framework employs a model for trust negotiation that is based on state machines, extended with security abstractions. Our policy model supports lifecycle management, an important trait in the dynamic environments that characterize Web services. In particular, we provide a set of change operations to modify policies, and migration strategies that permit ongoing negotiations to be migrated to new policies without being disrupted. Experimental results show the performance benefit of these strategies. The proposed approach has been implemented as a container-centric mechanism that is transparent to the Web services and to the developers of Web services, simplifying Web service development and management as well as enabling scalable deployments.

Web services are emerging as a promising technology for the automation of inter-organizational interactions. As technology matures and the foundations of Web services become more solid, users will start to demand tools that facilitate the service development lifecycle. It is only when such tools become available that novel technologies become applied and enter the mainstream, since the complexity, cost and time necessary to deploy and manage solutions is dramatically reduced. In this paper, we present a framework and a tool that support the model-driven development of Web services. The idea consists in identifying key Web services abstractions, in addition to those of basic Web services standards, that enable the description of service policies and properties that are useful in practice. In this paper, we focus on service protocols, and specifically on conversation and trust negotiation protocols. These protocols are modeled by means of graphical tools and high-level languages so that they are easy to specify, understand, and evolve. The tools also support the automatic generation of service implementation skeletons based on these abstractions, manage the entire service lifecycle, and provide run-time support to verify that the interaction among clients and services occur in compliance with the specified policies.

The Web is changing the way organizations are conducting their business. Businesses are rushing to provide modular applications, called Web services, that can be programmatically accessed through the Web. Despite the tremendous developments achieved so far, one of the most important, yet untapped potential, is the use of Web services as facilitators for inter-organizational cooperation. This promising concept, known as Web service composition, is gaining momentum as the potential silver bullet for the envisioned Semantic Web. The development of such integrated services has so far been ad hoc, time-consuming, and requires extensive low-level programming efforts. In this paper, we present WebBIS (Web Base of Internet-accessible Services), a generic framework for composing and managing Web services. We combine the object-oriented and active rules paradigms for such a task. We also provide a ontology-based framework for organizing the Web service space. We finally propose a peer-to-peer mechanism for reporting, propagating, and reacting to changes in Web services.

The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.

The development of new services through the integration of existing ones has gained a considerable momentum as a means to create and streamline business-to-business collaborations. Unfortunately, as Web services are often autonomous and heterogeneous entities, connecting and coordinating them in order to build integrated services is a delicate and time-consuming task. In this paper, we describe the design and implementation of a system through which existing Web services can be declaratively composed, and the resulting composite services can be executed following a peer-to-peer paradigm, within a dynamic environment. This system provides tools for specifying composite services through. statecharts, data conversion rules, and provider selection, policies. These specifications are then translated into XML documents that can be interpreted by peer-to-peer inter-connected software components, in order to provision the composite service without requiring a central authority.

The ubiquity of the World Wide Web offers an ideal opportunity for the deployment of highly distributed applications. Now that connectivity is no longer an issue, attention has turned to providing a middleware infrastructure that will sustain data sharing among Web-accessible databases. We present a dynamic architecture and system for describing, locating, and accessing data from Web-accessible databases. We propose the use of flexible organizational constructs service links and coalitions to facilitate data organization, discovery, and sharing among Internet-accessible databases. A language is also proposed to support the definition and manipulation of these constructs. The implementation combines Java, CORBA, database API (JDBC), agent, and database technologies to support a scalable and portable architecture interconnecting large networks of heterogeneous and autonomous databases. We report on an experiment to provide uniform access to a Web of healthcare-related databases.

We describe an architecture called WebFINDIT that allows dynamic couplings of Web accessible databases based on their content and interest. We propose an implementation using WWW, Java, JDBC, and CORBA's ORBs that communicate via the CORBA's IIOP protocol. The combination of these technologies offers a compelling middleware infrastructure to implement fluid-area enterprise applications. In addition to a discussion of WebFINDIT's core concepts and implementation architecture, we also discuss an experience of exiting WebFINDIT in a healthcare application.

article Free Access Share on World Wide Database—integrating the Web, CORBA and databases Authors: Athman Bouguettaya Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Boualem Benatallah Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Lily Hendra Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , James Beard Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Kevin Smith Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile , Mourad Quzzani Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 Australia Queensland University of Technology, School of Information Systems GPO Box 2434 Brisbane, QLD, 4001 AustraliaView Profile Authors Info & Claims ACM SIGMOD RecordVolume 28Issue 2June 1999 pp 594–596https://doi.org/10.1145/304181.304589Online:01 June 1999Publication History 4citation577DownloadsMetricsTotal Citations4Total Downloads577Last 12 Months4Last 6 weeks0 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

The paper addresses the design of a schema evolution framework enabling an efficient management of object versions. This framework is based on the adaptation and extension of two main schema evolution approaches, that is the approaches based on schema modification and those based on schema versioning. The framework provides an integrated environment to support different levels of adaptation (such as, modification and versioning at the schema level, conversion, object versioning, and emulation at the instance level). In addition, the authors introduce the concept of class/schema version pertinence enabling the database administrator to judge the pertinence of versions with regard the application programs. Finally, they provide operations for immediate refreshing of a database to enable an efficient manipulation of versions by a large number of application programs.