(Sumbitted to EDBT 2024)In this demo we introduce Headwork, an open-source academic platform for the crowdsourcing of complex tasks. Besides classical crowdsourcing features, Headwork eases the development of crowdsourcing campaigns through a full relational abstraction of relevant concepts (participants, skills, tasks, current answers, decision procedures, GUI, etc.). It allows in particular the orchestration of complex dynamic tasks using so-called tuple artifacts (i.e. finite-state automata which transition guards and actions are SQL-defined, on an evolving database). The demo will illustrates these key features, both from the participant and developer point of view.

Models at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (ie. values of properties of interest). DataTime provides a unifying interface to query the past, present and future (predicted) states of the system. This unifying interface provides i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, ii) the ability to get the last available value provided by the system's sensors, and iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can deprecate the use of heterogeneous tools for managing data from the past, the present and the future, and facilitate the development of digital twins.

Agglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering. We validate our work with extensive experiments on real data sets and compare it with existing tree-based methods, using a new measure of similarity between heterogeneous hierarchical structures.

Besides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets.

Recent advances in encrypted outsourced databases support the direct processing of queries on encrypted data. Depend- ing on functionality (i.e. operators) required in the queries the database has to use different encryption schemes with different security properties. Next to these functional re-quirements a security administrator may have to address security policies that may equally determine the used en-cryption schemes. We present an algorithm and tool set that determines an optimal balance between security and functionality as well as helps to identify and resolve possible conflicts. We test our solution on a database benchmark and business-driven security policies.

Being a Community manager is an emerging employment in social Web companies. His or her role is to monitor communities on a devoted social website, in order to understand new trends or behaviours. He or she also has to discover and attract new potent

In this paper, we study watermarking methods to prove the ownership of an ontology. Different from existing approaches, we propose to watermark not by altering existing statements, but by removing them. Thereby, our approach does not introduce false statements into the ontology. We show how ownership of ontologies can be established with provably tight probability bounds, even if only parts of the ontology are being re-used. We finally demonstrate the viability of our approach on real-world ontologies.

Watermarking allows robust and unobtrusive insertion of information in a digital document. During the last few years, techniques have been proposed for watermarking relational databases or Xml documents, where information insertion must preserve a specific measure on data (for example the mean and variance of numerical attributes). In this article we investigate the problem of watermarking databases or Xml while preserving a set of parametric queries in a specified language, up to an acceptable distortion. We first show that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or treelike structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.

We consider the class of applications that manage time series (TS) and propose a data model and a query language that let these applications manipulate TS data sets at a logical level. We introduce the concept of synchronized time series (STS) to model the alignment of several time series in a common time space. We show how this concept helps to analyze and compare information extracted from time series, and how it constitutes a convenient tool that supports an extended set of operations. The main contribution of the paper is a formal query language that combines generic operators on TS with user-defined functions. The language can be evaluated in closed form over STS data sets and gives rise to rewriting and optimization techniques. Throughout the paper, we illustrate our approach with several examples drawn from a few representative TS applications.

We propose an approach for producing database publishing programs by example. The main idea is to interactively build an example document, representative of the program output. The system infers from this document, without ambiguity, the publishing program. The end-user does not need to know a programming language, a query language or the database schema.

This paper presents a walermarking/fingerprinting system for relational databases. It features a built-in declarative language to specify usability constraints that watermarked data sets must comply with. For a subset of these constraints, namely, weight-independent constraints, we propose a novel watermarking strategy that consists of translating them into an integer linear program. We show two watermarking strategies: an exhaustive one based on integer linear programming constraint solving and a scalable pairing heuristic. Fingerprinting applications, for which several distinct watermarks need to be computed, benefit from the reduced computation time of our method that precomputes the watermarks only once. Moreover, we show that our method enables practical collusion-secure fingerprinting since the precomputed watermarks are based on binary alterations located at exactly the same positions. The paper includes an in-depth analysis of false-hit and false-miss occurrence probabilities for the detection algorithm. Experiments performed on our open source software WATERMILL assess the watermark robustness against common attacks and show that our method outperforms the existing ones concerning the watermark embedding speed.

Providing a fully functional multimedia DBMS (MMDBMS) becomes an emergency with the recent development of distributed environments. In this paper we address the impact of using watermarking techniques traditionally used to preserve the intellectual or industrial property (IIP) in MMDBMS. Through a multimedia content and metadata based representation model called M2, we particularly study: 1) how to watermark all components of a multimedia description, and not only its raw data 2) how watermarking can guarantee the mapping between multimedia objects and their descriptors, avoiding accidental or malevolent mismatch inside crucial documents 3) how to preserve data significance and semantics when altering data for watermarking purposes. We illustrate our approach by providing an example in the medical domain

Watermarking allows robust and unobtrusive insertion of information in a digital document. Very recently, techniques have been proposed for watermarking relational databases or XML documents, where information insertion must preserve a specific measure on data (e.g. mean and variance of numerical attributes.)In this paper we investigate the problem of watermarking databases or XML while preserving a set of parametric queries in a specified language, up to an acceptable distortion.We first observe that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or tree-like structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking.

We study the efficient approximation of queries in linear constraint databases using sampling techniques. We define the notion of an almost uniform generator for a generalized relation and extend the classical generator of Dyer, Frieze and Kannan for convex sets to the union and the projection of relations. For the intersection and the difference, we give sufficient conditions for the existence of such generators. We show how such generators give relative estimations of the volume and approximations of generalized relations as the composition of convex hulls obtained from the samples.