Integrating data from multiple sources with different native data models can be a demanding task that requires significant manual effort. Polystore is a new federated database view designed to enable polyglot querying across diverse data models. This paper aims to discuss the most significant security challenges associated with data federation. We identify the key privacy risks hindering polystore technologies and highlight the gap between traditional data protection approaches and current security issues. Moreover, we present a reference architecture as an active solution to significantly improve data usage in compliance with upstream security policies. Access control is the most widely-accepted reference in the database community for dealing effectively with this type of problem. In this context, we propose an access control mechanism that increases the resilience of the Polystore through Moving Target Defense (MTD) concept. Our approach is intended to address the security focus on privacy risks associated with Polystore technologies, and to enhance their adoption in modern data-driven applications.

Medical prevention is a very important aspect of healthcare informatics research through the prediction of medical events (e.g., disease diagnosis). In this work, we propose a deep learning approach to perform multi-label prediction on acts of medical care and treatments. The proposed approach utilizes a time-aware long short-term memory network and extends it with additional information from a fuzzy clustering of the same portfolio. The former mechanism (time-aware) is used to handle the temporal irregularity between the elements of a medical trajectory whereas the latter mechanism (fuzzy clustering) assists in modeling the heterogeneity among patients and treatments. Using a large portfolio of reimbursed medical records (over 16 million consumed acts of medical care) by a healthcare insurance in France, we show that our approach outperforms traditional and deep learning methods in medical multi-label prediction. Our work has implications for supporting medical prevention and more broadly improving the quality of healthcare services and insurance.

During the last decade, big data management has attracted increasing interest from both the industrial and academic communities. In parallel, Cyber Security has become mandatory due to various and more intensive threats. In June 2022, a group of researchers has met to reflect on their community’s impacts on current research challenges. In particular, they have considered four dimensions: (1) dedicated systems being data processing and analytic platforms or time series management systems; (2) graphs analytics and distributed computation; (3) privacy; and (4) new hardware.

Multi-target regression (MTR) aims at designing models able to predict multiple continuous variables simultaneously. The key for designing an effective feature selection model for MTR is to develop a framework under which the feature importances are measured using the correlation between features and targets in a natural way. So far, feature importances in MTR problems were evaluated in a global sense where proposed approaches generate a single ordered list of features common for all the targets. In this work, we adapt the Ensemble of Regressor Chains algorithm in tandem with the random forest paradigm to appropriately model both dependencies among features and targets in a target-specific (localized) feature ranking process. We provide empirical results on several benchmark MTR data sets indicating the effectiveness of our strategy to perform better than selecting one global ranking for all targets with existing state-of-the-art algorithms.

Multi-target regression MTR is a challenging task that consists of creating predictive models to predict multiple continuous targets simultaneously. The benefit of exploiting conditional target dependencies in MTR can in principle improve the generalization performance but is known to be closely dependent on the properties of the data and the type of loss to be minimized. In MTR data where many inter-dependencies between the targets may be present, explicitly modeling all inter-target and input-output relationships is intuitively far more reasonable. In this paper, we address this problem by designing a MTR approach based on Bayesian networks and demonstrate that using the Markov blanket graph of the multiple target variables to explicitly identify different target powersets and their optimal set of predictors can serve as a powerful learning framework for MTR. Experimental results on various benchmark MTR data sets approved that the proposed method enjoys significant advantages compared to other state-of-the-art MTR methods.

The location-based services (LBS) are gaining a great importance due to the tremendous development in telecommunications and geographic information systems. In addition, the spread of laptops and mobile devices that can use these services at anytime and anywhere helps the diffusion of this type of application. Nevertheless, there are significant challenges that could impede the use of this type of services. User's Privacy is considered as one of these important challenges that limit the usage of these services because this type of services is user location based. Thereby, different techniques are developed to preserve the user location privacy while s/he uses the LBS. Most of these techniques depend on the Kanonymity [11] of user's location by querying about a spatial region that contains k-1 users. Thus, the adversary cannot detect the user who asked for the service from these k users. However, if the adversary is in possession of some information about the users such as users' profiles and historical queries, s/he can discover in a high percentage the query issuer from the k users in the spatial region. In addition, if several users in a specified spatial area send at the same time their requests, the potential attacks targeting the privacy in the LBS could increase. In this paper, we investigate the impact of users' profiles and previously issued queries by the users in case of concurrent queries on the privacy. We propose an algorithm for predicating the issuer of each query and her/his underlying location in case of concurrent queries based on the users' profiles, historical queries, and the spatial area geographical characteristics. The experiments show that the concurrent queries affect negatively the privacy level in the location based services.

Big Data refers to a large and complex data. It has four characteristics: volume, variety, velocity, and veracity. Typically, there are different types of Big Data: structured, semi-structured, and unstructured out of which the last two pose challenges in applications such as query processing. Especially, the query processing on semi-structured data is enormously challenging. Big Data and its characteristics have been documented in a large volume of literature however a comprehensive discussion of the characteristics of a specific type of Big Data is missing. Therefore, a solid understanding of these characteristics is sine quo non to process complex queries efficiently. Big Data is a generic term. We do not categorise Big Data in this paper instead we focus only on Big Linked Data which we called Blinked Data. It is a variant of Big semi-structured data which has a set of characteristics that are critical to modeling and processing. In this paper, we investigate the characteristics and challenges of Blinked Data. This paper aims to provide a comprehensive description of the concept 'Blinked Data'. In addition, this research presents the challenges in processing queries on Blinked Data through an empirical study.

Information is more and more stored over distributed sources. A simple access to information in these sources requires a single access point. Data integration methods are designed to provide this kind of access by allowing to specify a mediator between the users and the sources. From an access control point of view, the question is how to specify access control of a system built on top of distributed data sources. Each source specifies and enforces its own policies. So, an access control enforced at the mediator level has to preserve the local access controls (of the sources). In this paper, we investigate an approach allowing to derive (i.e., factorization) the access control policies that should be attached and enforced at the mediator level. The proposed approach is illustrated on the relational data model as a reference framework. The approach ensures that the local policies are preserved at the mediator level.

Web services privacy issues have been attracting more and more attention in the past years. Since the number of Web services-based business applications is increasing, the demands for privacy enhancing technologies for Web services will also be increasing in the future. In this paper, we investigate an extension of business protocols, i.e. the specification of which message exchange sequences are supported by the web service, in order to accommodate privacy aspects and time-related properties. For this purpose we introduce the notion of Timed Privacy-aware Business Protocols (TPBPs). We also discuss TPBP properties can be checked and we describe their verification process.

Time is an important parameter in modeling and analyzing Web services. A Web service can be presented by its behavior which can be described by a business protocol representing the possible sequences of message exchanges. Automated analyses of timed Web services such as compatibility checking are very difficult and in some cases are not possible with the presence of implicit transitions (internal transitions) based on time constraints. The semantics of the implicit transitions is the source of this difficulty because most of well known modeling tools do not express this semantics (e.g., epsilon transition on the timed automata has a different semantics). This paper presents an approach for removing complex implicit transitions of the timed business protocols before performing the interoperability analysis without changing the semantics of the protocols.

In this paper, we investigate the combination of configuration and query rewriting for semantic Web service composition. Given a user query and a set of service descriptions, we rely on query rewriting to find services that implement the functionalities expressed in the user query (discovery stage). Then, we use configuration to capture dependencies between services, and to generate a set of composed Web services described as a directed acyclic graph, while maintaining validity with respect to business rules (orchestration stage).Finally, we propose a semantic ranking algorithm to rank results according to user preferences (classification stage).The techniques used in our approach take into account the semantics of concepts utilized to describe the elements (services, business rules, query and user preferences) involved in the composition process. We provide a formal approach and its implementation, together with experiments on Web services from different application domains.

The demonstration addresses the problem of monitoring the compliance of privacy agreement that spells out a consumer's privacy rights and how their private information must be handled by the service provider. We present a Privacy Agreement Monitoring system, an easy-to-use, and an efficient tool for tightly controlling the private data usage flow dynamically in the area of web services. Some reasoning can be made upon the observations to enhance the compliance of the privacy agreement and enrich the knowledge on misuses.

Recently, most enterprises adapt Web services technologies for designing and building complex inter-enterprise business applications. These applications are built by the coordination between set of Web services. Therefore, checking the compatibility between two services to guarantee that they can interact correctly is an important issue. In case of service update or replacement, there is a need for checking the replaceability to ensure that the new service is compatible with all the services which were compatible with the replaced one. These two type of checking are based on the services descriptions. Enriching services descriptions by including their behaviours is becoming more and more important. This behaviour can be described by business protocols representing the possible sequences of message exchanges. Since a lot of Web services use access control policies to restrict the access to authorized consumers, these policies should be a part of the service description. Checking compatibility and replaceability between Web services by analyzing their business protocols after assigning the access control policies is the main contribution of this work. Access control policies will be presented using ontology.

Business process management, service-oriented architectures and software back-engineering heavily rely on the fundamental processes of mining of processes and web service business protocols from log files. Model extraction and mining aim at the (re)discovery of the behavior of a running model implementation using solely its interaction and activity traces, and no a priori information on the target model. This paper presents an approach for correlating messages and extracting the business protocol of a web service in the realistic scenario in which correlation information is entirely absent from interaction and activity logs. Correlation is achieved through deterministic computations that result in an extremely efficient method whose extensive experiments have shown its solid reliability, robustness when dealing with complex structures, and very high performance and scalability. This approach and the underlying algorithms extend what is actually possible to achieve in the web service business protocol mining domain using incomplete and noisy data logs, and opens new horizons in back-engineering of web services. The theoretical and experimental results clearly show the leap forward achieved herein.

A novel framework for effectively and efficiently selecting fine-grained access control rules from a target relational database to the set of materialized views defined on such a database is presented and experimentally assessed in this paper, along with the main algorithm implementing the focal selection task, called VSP-Bucket. The proposed security framework introduces a number of research innovations, ranging from a novel Datalog-based syntax, and related semantics, aimed at modeling and expressing access control rules over relational databases to algorithm VSP-Bucket itself, which is a meaningful adaptation of a well-know view-based query re-writing algorithm for query optimization purposes. Our framework exposes a high flexibility, due to the fact it allows several classes of access control rules to be expressed and handled on top of large relational databases, and, at the same, it introduces high effectiveness and efficiency, as demonstrated by our comprehensive experimental evaluation and analysis of performance and scalability of algorithm VSP-Bucket.

Governing business compliance with regulations, laws, best practices, contracts, and the like is not an easy task, and so far there are only limited software products available that help a company to express compliance rules and to analyze its compliance state. We argue that todaypsilas SOA-based way of implementing and conducting business (e.g., using Web services and business process engines) lends itself very well to the development of a comprehensive compliance government solution that effectively aids companies in being compliant. In this paper, we contextualize the compliance problem in SOA-based businesses, we highlight which are the most salient research challenges that need to be addressed, and we describe our approach to compliance governance, spanning design, execution, and evaluation concerns.

To automate the analysis of service descriptions, [?] proposed a simple and expressive business protocol model (the specification of possible message exchange sequences) based on state machines, supporting rich timing constraints. Furthermore, developers of client applications need to be aware not only of functional aspects but also of non-functional aspects including privacy. In fact, the major concerns of a client are the disclosure of its personnel data conveyed during the message exchange. The aim of the paper is to study the ability of business protocol to handle the privacy and its time-related properties.

Uncertainty is one of the major challenges related to the semantic gap in multimedia data description and retrieval. It is due not only to errors and imprecisions in content classification but also to the extended range of user queries. In this paper, an extension of fuzzy conceptual graphs, suitable for handling uncertainty in visual event description and retrieval, is presented. We deal with two types of graphs according to the sources of uncertainty. An extension of fuzzy spatial and temporal relationships is defined to capture imprecision in video content spatiotemporal descriptions. Moreover, similarity measures and matching algorithms are defined to assess the degree of match between the descriptions of video objects and the event model and then to detect events within video segments.

Very often, enterprises are subject to changes. These changes require adaptations regarding business process, security components, database access, etc. In this paper we present a comprehensive (basic) approach for the controlled evolution of Web service business protocols in a cooperative Web services framework. A set of elementary change operators is proposed to capture the change of protocols and a selective process of change projection is adopted to predict potentially interesting changes to partners in order to maintain compatibility.

Behavior understanding and semantic interpretation of dynamic visual scenes have attracted a lot of attention in computer vision research community. Although the use of surveillance cameras has proliferated, the understanding of activities still remains complex. While users are mostly interested in high level and subjective semantics, only low level visual features can be extracted in a reliable way. This paper presents a novel framework for video guided behavior monitoring, built around the event modeling concept. It enables users to design their personal models of events combining elementary concept and low level features using expressive formalisms. The framework enables then detection of the events within video streams based on low level features extraction and manual annotations analysis, while taking in consideration uncertainty. Examples depicting content-based events modeling and detection from video surveillance are presented to illustrate the approach.

Business protocols are becoming a necessary part ofWeb services description [4]. The work presented in [4] investigates mechanisms for analyzing the compatibility and the substitution (i.e., replaceability) of Web services based on their functional properties. In this paper, we focus on the replaceability analysis.Whether a service can replace another depends not only on their functional properties but also on non functional requirements (e.g., privacy policies). We propose a privacy-aware protocol replaceability approach to extend the work presented in [4] by privacy properties. We introduce a rule-based privacy model and we extend business protocols, leading to what we call private business protocols. Finally, a private replaceability analysis of private business protocols is discussed. We mainly investigate compatibility issues, that is whether one private business protocol can support the same set of conversations with respect to the privacy requirements.

In this paper, we propose a framework for dynamic Web services customization. The basic idea consists to automatically transform Web services configurations, published in services directories, into other ones better suited for client needs. The goal of this automatic transformation, based mainly on dynamic Web services composition, is twofold. On the one hand, it makes it easier for Web services providers to perform services customization. On the other hand, it allows services requesters to obtain a Web service with the configurations that satisfy their preferences, even when those preferences are not explicitly published in Web services directories.

International audience

Multimedia data are applicable in various domains such as education, advertising, entertainment and communication. Multimedia data can be in the form of documents, and require adequate spatial and temporal presentation models. Sometimes, due to the noisy problems such as low bandwidth, or user preferences, replacing a media-element by another one can be achieved in order to provide the better quality of presentation. This paper deals with the problem of finding adequate multimedia presentation that fulfills spatio-temporal adaptation for switching between a set of alternative and semantically equivalent media elements.

International audience

Digital images and video clips are becoming popular due to the increase in the availability of consumer devices that capture them. Digital content is also growing over the Internet. Applications that benefit from video are education and training, marketing support, medical, etc. The increase of this digital content creates a need for user-friendly tools to browse through large volumes of digital material. However, there are two basic impediments to wider use of digital video. The first is cataloging, which includes video digitization, compression and annotation, and the second is the lack of fast and effective search and browse techniques for this massive video content. The authors are interested in this second problem. One method that they believe is promising is the augmentation of a metadatabase with information on video content so that users can be guided to appropriate data sets. An automated technique is presented that combines manual annotations and knowledge produced by an automatic content characterization technique (i.e. clustering algorithms) to build higher level abstraction of video content.

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.

Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.

1. SYSTEM TECHNICAL DESCRIPTION The ClassMiner system demonstrates a fully implemented tool for scalable video skimming and summarization. The key technology in the system is the integrated medical video content structure and events mining process, which was presented in a paper at the SIGMOD workshop on Data Mining and Knowledge Discovery [1]. As the system architecture in Fig. 1 indicates, we first apply a general video shot segmentation and key-frame selection scheme to parse the video stream into physical units. Then, the video group detection, scene detection and clustering strategies are executed to mine the video content structure. Various visual and audio feature processing techniques are utilized to detect some semantic cues, such as slides, face and speaker changes, etc. within the video, and these detection results are joined together to mine three types of events (presentation, dialog, clinical operation) from the detected video scenes. Finally, a scalable video skimming and summarization tool is constructed based on the mined video content structure and event information to help the user visualize and access video content.

In this paper we consider how constraint-based technology can be used to query semistructured data. We present a formalism based on feature logics for querying semistructured data. The formalism is a hybrid one in the sense that it combines clauses with path constraints. The resulting language has a clear declarative and operational semantics. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Indexing video data is essential for providing content-based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues, even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence, new solutions are significant. This paper develops a data model and a rule-based query language for video content-based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, and (2) video frames which contain these entities.

Indexing video data is essential for providing content based access. We consider how database technology can offer an integrated framework for modeling and querying video data. We develop a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (i.e., symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: the entities (i.e., objects) of interest in the domain of a video sequence; video frames which contain these entities. To represent this information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics.

Describes a knowledge-based approach for modelling and retrieving video data. Selected objects of interest in a video sequence are described and stored in a database. This database forms the object layer. On top of this layer, we define the schema layer that is used to capture the structured abstractions of the objects stored in the object layer. We propose two abstract languages on the basis of description logics: one for describing the contents of these layers and the other, which is more expressive, for making queries. The query language provides possibilities for navigation of the schema through forward and backward traversal of links, sub-setting of attributes and constraints on links.