As large Open Data are increasingly shared as RDF graphs today, there is a growing demand to help users discover the most interesting facets of a graph, which are often hard to grasp without automatic tools. We consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Our problem departs from analytics in relational data warehouses in that (i) in an RDF graph we are not given but we must identify the facts, dimensions, and measures of candidate aggregates; (ii) the classical approach to efficiently evaluating multiple aggregates breaks in the face of multi-valued dimensions in RDF data. In this work, we propose an extensible end-to-end framework that enables the identification and evaluation of interesting aggregates based on a new RDF-compatible one-pass algorithm for efficiently evaluating a lattice of aggregates and a novel early-stop technique (with probabilistic guarantees) that can prune uninteresting aggregates. Experiments using both real and synthetic graphs demonstrate the ability of our framework to find interesting aggregates in a large search space, the efficiency of our algorithms (with up to 2.9x speedup over a similar pipeline based on existing algorithms), and scalability as the data size and complexity grow.

RDF data is complex; exploring it is hard, and can be done through many different metaphors. We have developed and propose to demonstrate Spade, a tool helping users discover meaningful content of an RDF graph by showing them the results of aggregation (OLAP-style) queries automatically identified from the data. Spade chooses aggregates that are visually interesting , a property formally based on statistic properties of the aggregation query results. While well understood for relational data, such exploration raises multiple challenges for RDF: facts, dimensions and measures have to be identified (as opposed to known beforehand); as there are more candidate aggregates, assessing their interestingness can be very costly; finally, ontologies bring novel specific challenges but also novel opportunities, enabling ontology-driven exploration from an aggregate initially proposed by the system. Spade is a generic, extensible framework , which we instantiated with: ( i ) novel methods for enumerating candidate measures and dimensions in the vast space of possibilities provided by an RDF graph; ( ii ) a set of aggregate interestingness functions; ( iii ) ontology-based interactive exploration, and ( iv ) efficient early-stop techniques for estimating the interestingness of an aggregate query. The demonstration will comprise interactive scenarios on a variety of large, interesting RDF graphs.

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 "how far" 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.

The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. As a result, effective sense-making of semantically rich and big datasets has received a lot of attention, and new search approaches, such as Exploratory Computing (EC), have seen the light. In this paper we present IQ4EC, a system for data exploration inspired by EC, that supports users in the inspection of huge amounts of relational data through a step-by-step process, providing feedback based on approximate, intensional information expressed in terms of association rules. At each step of the process, the users can choose a portion of data to examine, and the system guides them to the next step by providing synthetic information and visualization of the resulting dataset.

The advent of the Big Data challenge has stimulated research on methods to deal with the problem of managing data abundance. Many approaches have been developed, but for the most part, they attack one specific side of the problem: e.g. efficient querying, analysis techniques that summarize data or reduce its dimensionality, data visualization, etc. The approach proposed in this poster aims instead at taking a comprehensive view: first of all, it supports human exploration as an iterative and multi-step process and therefore allows building upon a previous query on to the next, in a sort of "dialogue" between the user and the system. Second, it aims at supporting a variety of user experiences, like investigation, inspiration seeking, monitoring, comparison, decision-making, research, etc. Third, and probably most important, it adds to the notion of "big" the notion of "rich": Exploratory Computing (EC) aims at dealing with datasets of semantically complex items, whose inspection may reach beyond the user's previous knowledge or expectations: an exploratory experience basically consists in creating, refining, modifying, comparing various datasets in order to "make sense" of these meanings. A crucial challenge of EC lies at the user interface level (data visualization, feedback, relevance of the results, interaction possibilities): how to convey, in an effective manner, all the possible turn-takings of this "dialogue" between the user and the system.

The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. Many approaches have been developed, but for the most part, they attack one specific side of the problem: e.g. efficient querying, analysis techniques that summarize data or reduce its dimensionality, data visualization, etc. The approach proposed in this paper aims instead at taking a comprehensive view: first of all, it takes into account that human exploration is an iterative and multi-step process and therefore allows building upon a previous query on to the next, in a sort of “dialogue” between the user and the system. Second, it aims at supporting a variety of user experiences, like investigation, inspiration seeking, monitoring, comparison, decision-making, research, etc. Third, and probably most important, it adds to the notion of “big” the notion of “rich”: Exploratory Computing (EC) aims at dealing with datasets of semantically complex items, whose inspection may reach beyond the user's previous knowledge or expectations: an exploratory experience basically consists in creating, refining, modifying, comparing various datasets in order to “make sense” of these meanings.

Abstract FS-rules provide a powerful monotonic extension for Horn clauses that supports monotonic aggregates in recursion by reasoning on the multiplicity of occurrences satisfying existential goals. The least fixpoint semantics, and its equivalent least model semantics, hold for logic programs with FS-rules; moreover, generalized notions of stratification and stable models are easily derived when negated goals are allowed. Finally, the generalization of techniques such as seminaive fixpoint and magic sets, make possible the efficient implementation of DatalogFS, i.e., Datalog with rules with Frequency Support (FS-rules) and stratified negation. A large number of applications that could not be supported efficiently, or could not be expressed at all in stratified Datalog can now be easily expressed and efficiently supported in DatalogFS and a powerful DatalogFS system is now being developed at UCLA.

Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intensional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea-the gist-of both the structure and the content of the XML document and 2) quick, approximate answers to queries. In this paper, we focus on the second feature. A prototype system and experimental results demonstrate the effectiveness of the approach.