X-som: A flexible ontology mapper

System interoperability is a well known issue, especially for heterogeneous information systems, where ontologybased representations may support automatic and usertransparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies. Besides describing the tool components, we discuss the prototype implementation, which has been tested against the OAEI 2006 benchmark with promising results.

Operational and abstract semantics of the query language G-Log

The amount and variety of data available electronically have dramatically increased in the led decade; however, data and documents are stored in different ways and do notusual# show their internal structure. In order to take ful advantage of thetopolk9dQ# structure ofdigital documents, andparticulIII web sites, theirhierarchical organizationshouliz explizatio introducing a notion of querysimil; to the one usedin database systems. A good approach, in that respect, is the one provided bygraphical querylrydM#99; original; designed to model object bases and lndd proposed for semistructured data, la, G-Log. The aim of this paper is to providesuitabl graph-basedsemantics to thislisd;BI# supporting both data structure variabil#I andtopol#Ik;M similpol#I between queries and document structures. A suite ofoperational semantics basedon the notion ofbisimulQM#I is introduced both at theconcr--h level (instances) andat theabstru( level (schemata), giving rise to a semantic framework that benefits from the cross-fertil9;dl of tool originalM designed in quite different research areas (databases, concurrency,loncur static analysis)

Semi-automatic support for evolving functional dependencies

During the life of a database, systematic and frequent violations of a given constraint may suggest that the represented reality is changing and thus the constraint should evolve with it. In this paper we propose a method and a tool to (i) find the functional dependencies that are violated by the current data, and (ii) support their evolution when it is necessary to update them. The method relies on the use of confidence, as a measure that is associated with each dependency and allows us to understand \u201dhow far\u201d the dependency is from correctly describing the current data; and of goodness, as a measure of balance between the data satisfying the antecedent of the dependency and those satisfying its consequent. Our method compares favorably with literature that approaches the same problem in a different way, and performs effectively and efficiently as shown by our tests on both real and synthetic databases

A graph-based meta-model for heterogeneous data management

The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a meta-model based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described

Semantic pervasive advertising

Abstract. Pervasive advertising targets consumers on-the-move with ads displayed on their mobile devices. As for web advertising, ads are distributed by embedding them into websites and apps, easily flooding consumers with a large number of uninteresting offers. As the pervasive setting amplifies traditional issues such as targeting, cost, and privacy, we argue the need for a new perspective on the problem. We introduce PervADs, a privacy-preserving, user-centric, and pervasive ads-distribution platform which uses semantic technologies to reason about the consumer's context and the intended target of the ads

Database challenges for exploratory computing

Helping users to make sense of very big datasets is nowadays considered an important research topic. However, the tools that are available for data analysis purposes typically address professional data scientists, who, besides a deep knowledge of the domain of interest, master one or more of the following disciplines: mathematics, statistics, computer science, computer engineering, and programming. On the contrary, in our vision it is vital to support also different kinds of users who, for various reasons, may want to analyze the data and obtain new insight from them. Examples of these data enthusiasts [4, 9] are journalists, investors, or politicians: non-technical users who can draw great advantage from exploring the data, achieving new and essential knowledge, instead of reading query results with tons of records. The term data exploration generally refers to a data user being able to find her way through large amounts of data in order to gather the necessary information. A more technical definition comes from the field of statistics, introduced by Tukey [12]: with exploratory data analysis the researcher explores the data in many possible ways, including the use of graphical tools like boxplots or histograms, gaining knowledge from the way data are displayed. Despite the emphasis on visualization, exploratory data analysis still assumes that the user understands at least the basics of statistics, while in this paper we propose a paradigm for database exploration which is in turn inspired by the exploratory computing vision [2]. We may describe exploratory computing as the step-by-step “conversation” of a user and a system that “help each other” to refine the data exploration process, ultimately gathering new knowledge that concretely fullfils the user needs. The process is seen as a conversation since the system provides active support: it not only answers user’s requests, but also suggests one or more possible actions that may help the user to focus the exploratory session. This activity may entail the use of a wide range of different techniques, including the use of statistics and data analysis, query suggestion, advanced visualization tools, etc. The closest analogy [2] is that of a human-tohuman dialogue, in which two people talk, and continuously make reference to their lives, priorities, knowledge and beliefs, leveraging them in order to provide the best possible contribution to the dialogue. In essence, through the conversation they are exploring themselves as well as the information that is conveyed through their words. This exploration process therefore means investigation, exploration-seeking, comparison-making, and learning altogether. It is most appropriate for big collections of semantically rich data, which typically hide precious knowledge behind their complexity. In this broad and innovative context, this paper intends to make a significant step further: it proposes a model to concretely perform this kind of exploration over a database. The model is general enough to encompass most data models and query languages that have been proposed for data management in the last few years. At the same time, it is precise enough to provide a first formalization of the problem and reason about the research challenges posed to database researchers by this new paradigm of interaction