Cache-efficient sweeping-based interval joins for extended Allen relation predicates

We develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing

Finding unexplained human behaviors in Social Networks

Detection of human behavior in On-line Social Networks (OSNs) has become a very important challenge for a wide range of appli- cations, such as security, marketing, parent controls and so on, opening a wide range of novel research areas, which have not been fully addressed yet. In this paper, we present a two-stage method for finding unexplained (and potentially anomalous) behaviors in social networks. First, we use Markov chains to automatically learn from the social network graph a number of models of human behaviors (normal behaviors); the second stage applies an activity detection framework based on the concept of possible words to detect all unexplained activities with respect to the well-known behaviors. Some preliminary experiments using Facebook data show the approach efficiency and effectiveness. Copyright © (2014) by Universita Reggio Calabria & Centro di Competenza (ICT-SUD) All rights reserved

A dual mode breath sampler for the collection of the end-tidal and dead space fractions

This work presents a breath sampler prototype automatically collecting end-tidal (single and multiple breaths) or dead space air fractions (multiple breaths). This result is achieved by real time measurements of the CO2 partial pressure and airflow during the expiratory and inspiratory phases. Suitable algorithms, used to control a solenoid valve, guarantee that a Nalophan® bag is filled with the selected breath fraction even if the subject under test hyperventilates. The breath sampler has low pressure drop (< 0.5 kPa) and uses inert or disposable components to avoid bacteriological risk for the patients and contamination of the breath samples. A fully customisable software interface allows a real time control of the hardware and software status. The performances of the breath sampler were evaluated by comparing a) the CO2 partial pressure calculated during the sampling with the CO2 pressure measured off-line within the Nalophan® bag; b) the concentrations of four selected volatile organic compounds in dead space, end-tidal and mixed breath fractions.Results showed negligible deviations between calculated and off-line CO2 pressure values and the distributions of the selected compounds into dead space, end-tidal and mixed breath fractions were in agreement with their chemical-physical properties

SemTree: An index for supporting semantic retrieval of documents

In this paper, we propose SemTree, a novel semantic index for supporting retrieval of information from huge amount of document collections, assuming that semantics of a document can be effectively expressed by a set of (subject, predicate, object) statements as in the RDF model. A distributed version of KD-Tree has been then adopted for providing a scalable solution to the document indexing, leveraging the mapping of triples in a vectorial space. We investigate the feasibility of our approach in a real case study, considering the problem of finding inconsistencies in documents related to software requirements and report some preliminary experimental results

Finding unexplained activities in time-stamped observation data

The activity recognition is a very big challenge for the entire research community. Thus, there are already numerous techniques able to find occurrences of activities in time-stamped observation data (e.g., a video, a sequence of transactions at a website, etc.) with each occurrence having an associated probability. However, all these techniques rely on models encoding a priori knowledge of either normal or malicious behavior. They cannot deal with events such as “zero day” attacks that have never been seen before. In practice, all these methods are incapable of quantifying how well available models explain a sequence of events observed in an observation stream. By the way, the goal of this thesis is different: in order to address the issue listed above, we want to find the subsequences of the observation data, called unexplained sequences, that known models are not able to “explain” with a certain confidence. Thus, we start with a known set A of activities (both innocuous and dangerous) that we wish to monitor and we wish to identify “unexplained” subsequences in an observation sequence that are poorly explained (e.g., because they may contain occurrences of activities that have never been seen or anticipated before, i.e. they are not in A). We formally define the probability that a sequence of observations is unexplained (totally or partially) w.r.t. A. We develop efficient algorithms to identify the top-k Totally and Partially Unexplained Activities w.r.t. A. These algorithms leverage theorems that enable us to speed up the search for totally/partially unexplained activities. We describe experiments using real-world video and cyber security datasets showing that our approach works well in practice in terms of both running time and accuracy

The contributions of sleep-related risk factors to diurnal car accidents

This study was intended to estimate the presence and number of individual sleep-related risk factors in a sample of diurnal car accidents and to analyze the extent to which these risk factors tended to be more represented in diurnal accidents involving only one vehicle, involving young drivers or occurring on non-urban roads. Two hundred fifty-three drivers involved in diurnal accidents were interviewed immediately after the accidents to assess their sleepiness-related personal conditions and the circumstances prior to the accident (i.e., individual sleep-related risk factors), such as poor sleep, changes in habitual sleeping patterns, prolonged wakefulness, self-reported acute sleepiness and daytime sleepiness, night-shift jobs and insomnia. A total of 12.3% of the drivers were classified as having at least one of the seven risk factors assessed in the study, supporting the general notion that drivers' sleepiness conditions are crucial, even in diurnal driving circumstances in which they are less likely to depend on chrono-biological processes. Furthermore, consistent with the guiding hypotheses, specific sleep-related risk factors were more evident in single (vs. multiple) car accidents, among young drivers and in car accidents occurring on non-urban roads. In summary, sleep-related risk factors seemed to have a negative impact on drivers' safety in circumstances of diurnal driving, especially when the accidents involved young individuals and occurred on non-urban roads. © 2012 Published by Elsevier B.V. All rights reserved

An RDF-Based Framework for Semantic Indexing of Web Pages

Managing efficiently and effectively very large amount of digital documents requires the definition of indexes able to capture and express documents' semantics. In this work, we propose an RDF based framework for semantic indexing of web pages considering the related textual information. In particular, we propose to capture the semantic nature of a given document, commonly expressed in natural language, by retrieving a number of RDF triples and to semantically index the documents on the base of meaning of the triples' elements (i.e. subject, verb, object). Preliminary experiments are reported to evaluate the proposed index strategy