Initially designed for Knowledge Graphs (KG) containing plain (s, p, o) triples, research on link prediction has evolved to address more complex structures such as hypergraphs, hyper-relational graphs, and bi-level graphs. These advancements push link prediction to handle annotation patterns where a triple is annotated with another (t, p, t), or where a triple is annotated with qualifiers (t, p, o). However, each approach focuses on a single annotation pattern, and the case where a triple is annotated with a subject (s, p, t) has never been explored. In this paper, we propose LP4P, the first link prediction model capable of predicting entities in both plain triples and three annotation patterns. LP4P captures the information expressed in annotations through a four-pattern attention layer, and its loss function further leverages the ontological information of KGs. Moreover, we built WD4P, an RDF KG derived from baseline datasets, which includes the four patterns. Extensive empirical evaluations demonstrate that LP4P outperforms relevant state-of-the-art models on knowledge graphs with the four patterns, and achieves comparable performance on standard benchmarks.

Scientific workflows have established themselves as valuable tools for designing, automating, and sharing scientific experiments and analyses, with the aim of promoting reproducibility and reuse. However, early evidence from a decade-old experiment revealed that a significant portion of workflows suffer from decay, making them difficult to rerun, let alone repeat or reproduce successfully. In this paper, we report on the results of an empirical analysis that offers a fresh perspective on this issue by examining workflows specified using workflow languages that are currently popular, namely SnakeMake and Nextflow. Our analysis seeks to address several key questions: Is workflow decay still prevalent, and have its causes evolved from the previous generations? What mechanisms and techniques can mitigate it?

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behaviour (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.

International audience

Today, large amounts of data are collected in various domains, presenting unprecedented economic and societal opportunities. Yet, at present, the exploitation of these data sets through data science methods is primarily dominated by AI-savvy users. From an inclusive perspective, there is a need for solutions that can democratise data science that can guide non-specialists intuitively to explore data collections and extract knowledge out of them. This paper introduces the vision of a new data science engine, called DS4ALL (Data Science for ALL), that empowers users who are neither computer nor AI experts to perform sophisticated data exploration and analysis tasks. Therefore, DS4ALL is based on a conversational and intuitive approach that insulates users from the complexity of AI algorithms. DS4ALL allows a dialogue-based approach that gives the user greater freedom of expression. It will enable them to communicate using natural language without requiring a high level of expertise on data-driven algorithms. User requests are interpreted and handled internally by the system in an automated manner, providing the user with the required output by masking the complexity of the data science workflow. The system can also collect feedback on the displayed results, leveraging these comments to address personalized data analysis sessions. The benefits of the envisioned system are discussed, and a use case is also presented to describe the innovative aspects.

International audience

Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.

International audience

Data lakes have recently emerged as an alternative solution to costly traditional data warehouse solutions. To exploit data lakes, however, there is a need for means that assist users in combining and integrating data stored within a data lake. In this paper, we position ourselves in the recurrent context where a user has a local dataset that is not sufficient for processing the queries that are of interest to him/her. We show how data lakes, or more specifically the service lakes, since we are focusing on data providing services, can be leveraged to answer user queries, taking into account the quality of the services and respecting the (time and monetary) budget set by the user.

Emails represent a valuable source of information that can be harvested for understanding undocumented business processes of institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs to make benefit of the many available process mining techniques. In this work, we go further in this direction, by proposing a new method for mining process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.

Scientific discovery and analysis are increasingly computational and data-driven. While scripting languages, such as Python, R and Perl, are the means of choice of the majority of scientists to encode and run their data analysis, scripts are generally not amenable to reuse or reproducibility. Scripts do rarely get reused or even shared with third party scientists. We argue in this paper that the reproducibility of scripts can be promoted by converting them into workflow research objects. A workflow research object encodes a script into a production (executable) workflow that is accompanied by annotations, example datasets and provenance traces of their execution, thereby allowing third party users to understand the data analysis encoded by the original script, run the associated workflow using the same or different dataset, or even repurpose it for a different analysis. To this end, we present a methodology for converting scripts into workflow research objects in a principled manner, guided by requirements that we elicited for this purpose. The methodology exploits tools and standards that have been developed by the community, in particular YesWorkflow, Research Objects and the W3C PROV. It is showcased using a real world use case from the field of Molecular Dynamics.

Data exchange is one of the oldest database problems, being of both practical and theoretical interest. Given the pace at which heterogeneous data are published on the web, thanks to initiatives such as Linked Data and Open Science, scalability of data exchange becomes crucial. Pivotal to data exchange is the chase algorithm, which is a fixpoint algorithm to evaluate both source-to-target constraints and target constraints in the data exchange process. In this paper, we investigate how new programming models such as MapReduce can be used to implement the chase on large-scale data sources. To the best of our knowledge, how to exchange data at scale has not been investigated so far. We present an initial solution for chasing source-to-target tuple generating dependencies and target tuple-generating dependencies, and discuss open issues that need to be addressed to leverage MapReduce for the data exchange problem.

Technical developments, such as the web of data and web data extraction, combined with policy developments such as those relating to open government or open science, are leading to the availability of increasing numbers of data sources. Indeed, given these physical sources, it is then also possible to create further virtual sources that integrate, aggregate or summarise the data from the original sources. As a result, there is a plethora of data sources, from which a small subset may be able to provide the information required to support a task. The number and rate of change in the available sources is likely to make manual source selection and curation by experts impractical for many applications, leading to the need to pursue a pay-as-you-go approach, in which crowds or data consumers annotate results based on their correctness or suitability, with the resulting annotations used to inform, e.g., source selection algorithms. However, for pay-as-you-go feedback collection to be cost-effective, it may be necessary to select judiciously the data items on which feedback is to be obtained. This paper describes OLBP (Ordering and Labelling By Precision), a heuristics-based approach to the targeting of data items for feedback to support mapping and source selection tasks, where users express their preferences in terms of the trade-off between precision and recall. The proposed approach is then evaluated on two different scenarios, mapping selection with synthetic data, and source selection with real data produced by web data extraction. The results demonstrate a significant reduction in the amount of feedback required to reach user-provided objectives when using OLBP.

It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits community- and crowd-driven information integration can derive from clustering.

Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions. In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects—aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington’s disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.

A major issue that arises when designing scientific experiments(i.e., workflows) is that of identifying the modules (which are of-ten “black boxes”), that are suitable for performing the steps of theexperiment. To assist scientists in the task of identifying suitablemodules, semantic annotations have been proposed and used to de-scribe scientific modules. Different facets of the module can be de-scribed using semantic annotations. Our experience with scientistsfrom modern sciences such as bioinformatics, biodiversity and as-tronomy, however, suggests that most of semantic annotations thatare available are confined to the description of the domain of inputand output parameters of modules. Annotations specifying the be-havior of the modules, as to the tasks they play, are rarely specified.To address this issue, we argue in this paper that data examples arean intuitive and effective means for understanding the behavior ofscientific modules. We present a heuristic for automatically gener-ating data examples that annotate scientific modules without rely-ing on the existence of the module specifications, and show throughan empirical evaluation that uses real-world scientific modules theeffectiveness of the heuristic proposed.The data examples generated can be utilized in a range of scientificmodule management operations. To demonstrate this, we presentthe results of two real-world exercises that show that: (i) Data ex-amples are an intuitive means for human users to understand thebehavior of scientific modules, and that (ii) data examples are aneffective ingredient for matching scientific modules.

Semantic annotation of web services has been proposed as a solution to the problem of discovering services to fit a particular need and reusing them appropriately. While there exist tools that assist human users in the annotation task, e.g., Radiant and Meteor-S, no semantic annotation proposal considers the problem of verifying the accuracy of the resulting annotations. Early evidence from workflow compatibility checking suggests that the proportion of annotations that contain some form of inaccuracy is high, and yet no tools exist to help annotators to test the results of their work systematically before they are deployed for public use. In this paper, we adapt techniques from conventional software testing to the verification of semantic annotations for web service input and output parameters. We present an algorithm for the testing process and discuss ways in which manual effort from the annotator during testing can be reduced. We also present two adequacy criteria for specifying test cases used as input for the testing process. These criteria are based on structural coverage of the domain ontology used for annotation. The results of an evaluation exercise, based on a collection of annotations for bioinformatics web services, show that defects can be successfully detected by the technique.

Provenance, a form of structured metadata designed to record the origin or source of information, can be instrumental in deciding whether information is to be trusted, how it can be integrated with other diverse information sources, and how to establish attribution of information to authors throughout its history. The PROV set of specifications, produced by the World Wide Web Consortium (W3C), is designed to promote the publication of provenance information on the Web, and offers a basis for interoperability across diverse provenance management systems. The PROV provenance model is deliberately generic and domain-agnostic, but extension mechanisms are available and can be exploited for modelling specific domains. This tutorial provides an account of these specifications. Starting from intuitive and informal examples that present idiomatic provenance patterns, it progressively introduces the relational model of provenance along with the constraints model for validation of provenance documents, and concludes with example applications that show the extension points in use.

While workflow technology has gained momentum in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing existing workflows to build new scientific experiments is still a daunting task. This is partly due to the difficulty that scientists experience when attempting to understand existing workflows, which contain several data preparation and adaptation steps in addition to the scientifically significant analysis steps. One way to tackle the understandability problem is through providing abstractions that give a high-level view of activities undertaken within workflows. As a first step towards abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna and Wings systems. Our analysis has resulted in a set of scientific workflow motifs that outline i) the kinds of data intensive activities that are observed in workflows (data oriented motifs), and ii) the different manners in which activities are implemented within workflows (workflow oriented motifs). These motifs can be useful to inform workflow designers on the good and bad practices for workflow development, to inform the design of automated tools for the generation of workflow abstractions, etc.

Workflows provide a popular means for preserving scientific methods by explicitly encoding their process. However, some of them are subject to a decay in their ability to be re-executed or reproduce the same results over time, largely due to the volatility of the resources required for workflow executions. This paper provides an analysis of the root causes of workflow decay based on an empirical study of a collection of Taverna workflows from the myExperiment repository. Although our analysis was based on a specific type of workflow, the outcomes and methodology should be applicable to workflows from other systems, at least those whose executions also rely largely on accessing third-party resources. Based on our understanding about decay we recommend a minimal set of auxiliary resources to be preserved together with the workflows as an aggregation object and provide a software tool for end-users to create such aggregations and to assess their completeness.

Scientific workflows are increasingly gaining momentum as the new paradigm for modeling and enacting scientific experiments. The value of a workflow specification does not end once it is enacted. Indeed, workflow specifications encapsulate knowledge that documents scientific experiments, and are, therefore, worth preserving. Our experience suggests that workflow preservation is frequently hampered by the volatility of the constituent service operations when these operations are supplied by third-party providers. To deal with this issue, we propose a heuristic for locating substitutes that are able to replace unavailable service operations within workflows. The proposed method uses the data links connecting inputs and outputs of service operations in existing workflow specifications to locate operations with parameters compatible with those of the missing operations. Furthermore, it exploits provenance traces collected from past executions of workflows to ensure that candidate substitutes perform tasks similar to those of the missing operations. The effectiveness of the proposed method has been empirically assessed.

The vision of dataspaces proposes an alternative to classical data integration approaches with reduced up-front costs followed by incremental improvement on a pay-as-you-go basis. In this paper, we demonstrate DSToolkit, a system that allows users to provide feedback on results of queries posed over an integration schema. Such feedback is then used to annotate the mappings with their respective precision and recall. The system then allows a user to state the expected levels of precision (or recall) that the query results should exhibit and, in order to produce those results, the system selects those mappings that are predicted to meet the stated constraints.

The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-as-you-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for query evaluation considering user requirements in terms of precision and recall. In doing so, we cast mapping selection as an optimization problem. Mapping annotations may reveal that the quality of schema mappings is poor. We also show how feedback can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. The results of evaluation exercises show the effectiveness of our solution for annotating, selecting and refining schema mappings.

The management and querying of workflow provenance data underpins a collection of activities, including the analysis of workflow results, and the debugging of workflows or services. Such activities require efficient evaluation of lineage queries over potentially complex and voluminous provenance logs. Näive implementations of lineage queries navigate provenance logs by joining tables that represent the flow of data between connected processors invoked from workflows. In this paper we provide an approach to provenance querying that: (i) avoids joins over provenance logs by using information about the workflow definition to inform the construction of queries that directly target relevant lineage results; (ii) provides fine grained provenance querying, even for workflows that create and consume collections; and (iii) scales effectively to address complex workflows, workflows with large intermediate data sets, and queries over multiple workflows.

Computational and data-intensive science increasingly depends on a large Web Service infrastructure, as services that provide a broad array of functionality can be composed into workflows to address complex research questions. In this context, the goal of service registries is to offer accurate search and discovery functions to scientists. Their effectiveness, however, depends not only on the model chosen to annotate the services, but also on the level of abstraction chosen for the annotations. The work presented in this paper stems from the observation that current annotation models force users to think in terms of service interfaces, rather than of high-level functionality, thus reducing their effectiveness. To alleviate this problem,we introduce Functional Units (FU) as the elementary units of information used to describe a service. Using popular examples of services for the Life Sciences, we define FUs as configurations and compositions of underlying service operations, and show how functional-style service annotations can be easily realised using the OWL semantic Web language. Finally, we suggest techniques for automating the service annotations process, by analysing collections of workflows that use those services.

There seems to be a general consensus on the crucial role metadata can play for enhancing the functionalities of scientific workflows systems, e.g., workflow and service discovery, composition and provenance browsing, among others. However, in most cases their management is under-specified, if not left unaddressed at all. A step in this direction, the main contribution of the work presented in this paper is an overview of metadata and their management in the Taverna workflow system. In Taverna, we consider metadata to be a first class citizen in the system, in the sense that we fully cover their life cycle from their creation, through their use and curation until their eventual removal. We present the main steps of this cycle and present the models used for metadata specification. In doing so, we distinguish two classes of metadata: metadata that describe workflow related entities, such as services, workflows and sub- workflows, and metadata that describe workflow executions, also known as workflow provenance.

MOTIVATION: The Functional Genomics Experiment Object Model (FuGE) supports modelling of experimental processes either directly or through extensions that specialize FuGE for use in specific contexts. FuGE applications commonly include components that capture, store and search experiment descriptions, where the requirements of different applications have much in common. RESULTS: We describe a toolkit that supports data capture, storage and web-based search of FuGE experiment models; the toolkit can be used directly on FuGE compliant models or configured for use with FuGE extensions. The toolkit is illustrated using a FuGE extension standardized by the proteomics standards initiative, namely GelML. AVAILABILITY: The toolkit and a demonstration are available at http://code.google.com/p/fugetoolkit

Semantic annotations of web services can support the effective and efficient discovery of services, and guide their composition into workflows. At present, however, the practical utility of such annotations is limited by the small number of service annotations available for general use. Manual annotation of services is a time consuming and thus expensive task, so some means are required by which services can be automatically (or semi-automatically) annotated. In this paper, we show how information can be inferred about the semantics of operation parameters based on their connections to other (annotated) operation parameters within tried-and-tested workflows. Because the data links in the workflows do not necessarily contain every possible connection of compatible parameters, we can infer only constraints on the semantics of parameters. We show that despite their imprecise nature these so-called loose annotations are still of value in supporting the manual annotation task, inspecting workflows and discovering services. We also show that derived annotations for already annotated parameters are useful. By comparing existing and newly derived annotations of operation parameters, we can support the detection of errors in existing annotations, the ontology used for annotation and in workflows. The derivation mechanism has been implemented, and its practical applicability for inferring new annotations has been established through an experimental evaluation. The usefulness of the derived annotations is also demonstrated.

Once tried-and-tested, scientific workflows can be, just like web services, stored in public repositories to be shared and reused by other scientists. In practice, however, the reuse of an existing workflow is frequently hampered by the volatility of its constituent service operations. To deal with this issue, we propose a method for locating service operations that can substitute the unavailable operations in a workflow based on a set of replaceability criteria that we formally define. The proposed method has been implemented as part of a tool for the automatic discovery of substitutes for unavailable service operations, and the evaluation of the proposed method against real world scientific workflows developed under the aegis of e- Science projects demonstrated its practical applicability.

Service oriented computing has gained a considerable momentum as a new paradigm for building enterprise information systems. Notable efforts have been made recently from both researchers and industrials to support the construction of service-based applications, nevertheless several issues still need to be tackled including service definition and adaptation, and services orchestration. This work proposes an approach for building and finely orchestrating open and adaptable services. An open service is represented by a workflow that coordinates calls to service provider methods. Thereby component activities and the way they are synchronized are rendered visible. Service adaptability refers to the possibility to modify an open service. Through adaptation operations a service can be customized according to given user (application) requirements. In order to finely orchestrate services, they are associated with entry points. An entry point acts as a gateway for inserting and getting information about the progress of service execution. Defined services and orchestration are verified to ensure a correct behaviour of the resulting application. The paper details our approach for building and orchestrating services, and presents associated architectural choices.

This paper presents TERRA and ACQUA, two mechanisms that support adaptable definition and execution of workflows. TERRA generates and stores structures (objects, events, reactions) necessary for executing a workflow. AQUA executes workflows according to a parametric behaviour model that represents workflow execution policies in terms of dimensions associated to values. TERRA and ACQUA provide mechanisms for modifying the structure of a workflow and its associated behaviour.

This paper introduces definition and enactment models to characterize workflows. The proposed knowledge model provides concepts for modeling activities, data used by such activities, agents, and ordering operators and synchronization modes used for specifying activities dependencies. The behavior model provides dimensions that characterize workflow enactment. Both models are currently implemented in our workflow management system called AFLOWS. The originality of our approach is related to its flexibility as different enactment models can be associated to a workflow simply by giving values to dimensions according to application needs. Such an approach and system provide a suitable support for specifying and enacting workflow processes that may be useful today to describe and monitor process-oriented applications (i.e., large Web-based applications that run across wide area networks and multiple enterprises).