166 research outputs found
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
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