In this system demonstration, we study views with binding patterns, which are a formalization of REST Web services. Such views are database queries that can be evaluated using the service, but only if values for the input variables are provided. We investigate how to use such views to answer a complex user query, by rewriting it as an execution plan, i.e., an orchestration of calls to the views. In general, it is undecidable to determine whether a given user query can be answered with the available views. In this demo, we illustrate a particular scenario studied in our earlier work [11], where the problem is not only decidable but has a particularly intuitive graphical solution. Our demo allows users to play with views defined by real Web services, and to animate the construction of execution plans visually.

Recent years have seen the rise of Web data, in particular Linked Data, with, up to now, more than 1000 datasets in the Linked Open Data Cloud (LOD). These datasets are mostly of entity-centric nature and are highly heterogeneous in terms of domains, language, schema, etc. Hence, the vision of uniformly querying such resources in the LOD has a long way to go. While equivalent entity instances across datasets are often linked by sameAs links, relations from different datasets and schemas are usually not aligned. In this paper, we propose an on-line instance-based relation alignment approach. The alignment may be performed during query execution and requires partial information from the datasets. We align relations to a target dataset using association rule mining approaches. We sample for equivalent entity instances with two main sampling strategies. Preliminary experiments, show that we are able to align relations with high accuracy, even if accessing the entire datasets is impossible or impractical.

The PIKM workshop offers Ph.D. students the opportunity to bring their work to an international and interdisciplinary research community, and create a network of young researchers to exchange and develop new and promising ideas. Similar to the CIKM, the PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management.

Many data providers make their data available through Web service APIs. In order to unleash the potential of these sources for intelligent applications, the data has to be combined across different APIs. However, due to the heterogeneity of schemas, the integration of different APIs remains a mainly manual task to date. In this paper, we model an API method as a view with binding patterns over a global RDF schema. We present an algorithm that can automatically infer the view definition of a method in the global schema. We also show how to compute transformation functions that can transform API call results into this schema. The key idea of our approach is to exploit the intersection of API call results with a knowledge base and with other call results. Our experiments on more than 50 real Web services show that we can automatically infer the schema with a precision of 81%-100%.

Newspapers are testimonials of history. The same is increasingly true of social media such as online forums, online communities, and blogs. By looking at the sequence of articles over time, one can discover the birth and the development of trends that marked society and history -- a field known as "Culturomics". But Culturomics has so far been limited to statistics on keywords. In this vision paper, we argue that the advent of large knowledge bases (such as YAGO [37], NELL [5], DBpedia [3], and Freebase) will revolutionize the field. If their knowledge is combined with the news articles, it can breathe life into what is otherwise just a sequence of words for a machine. This will allow discovering trends in history and culture, explaining them through explicit logical rules, and making predictions about the events of the future. We predict that this could open up a new field of research, "Semantic Culturomics", in which no longer human text helps machines build up knowledge bases, but knowledge bases help humans understand their society.

The API of a Web service restricts the types of queries that the service can answer. For example, a Web service might provide a method that returns the songs of a given singer, but it might not provide a method that returns the singers of a given song. If the user asks for the singer of some specific song, then the Web service cannot be called - even though the underlying database might have the desired piece of information. This asymmetry is particularly problematic if the service is used in a Web service orchestration system. In this paper, we propose to use on-the-fly information extraction to collect values that can be used as parameter bindings for the Web service. We show how this idea can be integrated into a Web service orchestration system. Our approach is fully implemented in a prototype called SUSIE. We present experiments with real-life data and services to demonstrate the practical viability and good performance of our approach.

The proliferation of knowledge-sharing communities and the advances in information extraction have enabled the construction of large knowledge bases using the RDF data model to represent entities and relationships. However, as the Web and its latently embedded facts evolve, a knowledge base can never be complete and up-to-date. On the other hand, a rapidly increasing suite of Web services provide access to timely and high-quality information, but this is encapsulated by the service interface. We propose to leverage the information that could be dynamically obtained from Web services in order to enrich RDF knowledge bases on the fly whenever the knowledge base does not suffice to answer a user query.

We present ANGIE, a system that can answer user queries by combining knowledge from a local database with knowledge retrieved from Web services. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate Web services to retrieve the missing information. This information is integrated seamlessly and transparently into the local database, so that the user can query and browse the knowledge base while appropriate Web services are called automatically in the background.

We study the scalable management of XML data in P2P networks based on distributed hash tables (DHTs). We identify performance limitations in this context, and propose an array of techniques to lift them. First, we adapt the DHT platform's index store and communication primitives to the needs of massive data processing. Second, we introduce a distributed hierarchical index and associated efficient algorithms to speed up query processing. Third, we present an innovative, XML-specific flavor of Bloom filters, to reduce data transfers entailed by query processing. Our approach is fully implemented in the KadoP system, used in a real-life software manufacturing application. Our experiments demonstrate the benefits of the proposed techniques.

The current abundance and complexity of P2P architectures makes it extremely difficult to assess their performance. P2PTester is the first tool devised to interface with, and measure the performance of, existing P2P data management platforms. We isolate basic components present in current P2P platforms, and insert "hooks" for P2PTester to capture, analyze and trace the interactions taking place in the underlying distributed system.

We present KADOP, a distributed infrastructure for warehousing XML resources in a peer-to-peer framework. KADOP allows users to build a shared, distributed repository of resources such as XML documents, semantic information about such documents, Web services, and collections of such items. KADOP leverages several existing technologies and models: it uses distributed hash tables as a peer communication layer, and ActiveXML as a model for constructing and querying the resources in the peer network.

In this paper, we study query evaluation on Active XML documents (AXML for short), a new generation of XML documents that has recently gained popularity. AXML documents are XML documents whose content is given partly extensionally, by explicit data elements, and partly intensionally, by embedded calls to Web services, which can be invoked to generate data. A major