2,522 research outputs found
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Finding relational redescriptions
We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept.
We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline Inductive Logic Programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks
ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware
Billions of users rely on the security of the Android platform to protect
phones, tablets, and many different types of consumer electronics. While
Android's permission model is well studied, the enforcement of the protection
policy has received relatively little attention. Much of this enforcement is
spread across system services, taking the form of hard-coded checks within
their implementations. In this paper, we propose Authorization Check Miner
(ACMiner), a framework for evaluating the correctness of Android's access
control enforcement through consistency analysis of authorization checks.
ACMiner combines program and text analysis techniques to generate a rich set of
authorization checks, mines the corresponding protection policy for each
service entry point, and uses association rule mining at a service granularity
to identify inconsistencies that may correspond to vulnerabilities. We used
ACMiner to study the AOSP version of Android 7.1.1 to identify 28
vulnerabilities relating to missing authorization checks. In doing so, we
demonstrate ACMiner's ability to help domain experts process thousands of
authorization checks scattered across millions of lines of code
Building and Maintaining Halls of Fame over a Database
Halls of Fame are fascinating constructs. They represent the elite of an
often very large amount of entities---persons, companies, products, countries
etc. Beyond their practical use as static rankings, changes to them are
particularly interesting---for decision making processes, as input to common
media or novel narrative science applications, or simply consumed by users. In
this work, we aim at detecting events that can be characterized by changes to a
Hall of Fame ranking in an automated way. We describe how the schema and data
of a database can be used to generate Halls of Fame. In this database scenario,
by Hall of Fame we refer to distinguished tuples; entities, whose
characteristics set them apart from the majority. We define every Hall of Fame
as one specific instance of an SQL query, such that a change in its result is
considered a noteworthy event. Identified changes (i.e., events) are ranked
using lexicographic tradeoffs over event and query properties and presented to
users or fed in higher-level applications. We have implemented a full-fledged
prototype system that uses either database triggers or a Java based middleware
for event identification. We report on an experimental evaluation using a
real-world dataset of basketball statistics
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty
There is a growing need for methods which can capture uncertainties and
answer queries over graph-structured data. Two common types of uncertainty are
uncertainty over the attribute values of nodes and uncertainty over the
existence of edges. In this paper, we combine those with identity uncertainty.
Identity uncertainty represents uncertainty over the mapping from objects
mentioned in the data, or references, to the underlying real-world entities. We
propose the notion of a probabilistic entity graph (PEG), a probabilistic graph
model that defines a distribution over possible graphs at the entity level. The
model takes into account node attribute uncertainty, edge existence
uncertainty, and identity uncertainty, and thus enables us to systematically
reason about all three types of uncertainties in a uniform manner. We introduce
a general framework for constructing a PEG given uncertain data at the
reference level and develop highly efficient algorithms to answer subgraph
pattern matching queries in this setting. Our algorithms are based on two novel
ideas: context-aware path indexing and reduction by join-candidates, which
drastically reduce the query search space. A comprehensive experimental
evaluation shows that our approach outperforms baseline implementations by
orders of magnitude
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
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