The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the Machine Learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.

Location-Based Social Networks can be profitably exploited to characterize citizens’ activities in urban environments. However, collecting LBSN is potentially challenging due to privacy concerns, connectivity issues, and potential imbalances in LBSN service usage. We propose to complement LBSN data with mobility data in the analysis of citizens’ activities in urban areas. Unlike the explicit insights provided by LBSN users, mobility data give implicit feedback on citizens’ habits. This paper explores the spatial and temporal conditions under which user habits are coherent according to both sources and reports the most reliable common sequences of visited categories of Points-Of-Interests. To this aim, it relies on a multidimensional model in which recurrent citizens’ activities are described by a new pattern type, namely the generalized activity pattern. It also detects the eventual presence of bias between LBSN and mobility user activities by customizing the established Statistical Parity metric. The motivations behind the detected bias are explained in terms of combinations of POI categories that are most likely to be the main causes. We evaluate the proposed approach on real-world data achieved from Foursquare check-ins, taxi service, and free-floating car sharing. The results highlight not only the complementarity of the data sources regarding specific POI categories, but also their interchangeability in many spatio-temporal conditions.

During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.

The spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.

Today's citizens and city administrations have an increasing interest in monitoring the air quality in urban areas. Studying the causes of air pollution entails analyzing the correlations between heterogeneous data, among which pollutant concentrations, traffic flow measurements, and meteorological data. To this end, innovative data analytics solutions able to acquire, integrate, and analyze very large amounts of data are needed. This paper presents a new data mining system, named GEneralized Correlation analyzer of pOllution data (GECKO), to discover interesting and multiple-level correlations among a large variety of open air pollution-related data. Specifically, correlations among pollutant levels and traffic and climate conditions are discovered and analyzed at different abstraction levels. The knowledge extraction process is driven by a taxonomy to generalize low-level measurement values as the corresponding categories. To ease the manual inspection of the result, the extracted correlations are classified into few classes based on the semantics of underlying data. The experiments, performed on real data acquired in a major Italian Smart City, demonstrate the effectiveness of the proposed analytics engine in discovering correlations among pollutant data that are potentially useful for supporting city administrators in decision-making.

Cardiopulmonary exercise testing is a non-invasive method widely used to monitor various physiological signals, describing the cardiac and respiratory response of the patient to increasing workload. Since this method is physically very demanding, innovative data analysis techniques are needed to predict patient response thus lowering body stress and avoiding cardiopulmonary overload. This paper proposes the Cardiopulmonary Response Prediction (CRP) framework for early predicting the physiological signal values that can be reached during an incremental exercise test. The learning phase creates different models tailored to specific conditions (i.e., single-test and multiple-test models). Each model can be exploited in the real-time stream prediction phase to periodically predict, during the test execution, signal values achievable by the patient. Experimental results on a real dataset showed that CRP prediction is performed with a limited and acceptable error.

Physicians and health care organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, analyzing these datasets to figure out interesting and hidden knowledge is a challenging task. This article proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules (i.e., nonredundant generalized rules) is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion: from the small subset of high-level rules, a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.

In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., the Misleading Generalized Itemsets, on a distributed computing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.

Gene Expression Datasets (GEDs) usually consist of the expression values of thousands of genes within hundreds of samples. Frequent itemset and association rule mining algorithms have been applied to discover significant co-expressions among multiple genes from GEDs. To perform these data analyses, gene expression values are commonly discretized into a predefined number of bins. Such an expert-driven and not trivial preprocessing step could bias the quality of the mining result. This paper presents a novel approach to discovering gene correlations from GEDs which does not require data discretization. By representing per-sample gene expression values as item weights, frequent weighted itemsets can be extracted. The discovery of weighted itemsets instead of traditional (not weighted) ones prevents experts from discretizing GEDs before analyzing them and thus improves the effectiveness of the knowledge discovery process. Experiments performed on real GEDs demonstrate the effectiveness of the proposed approach.

Incremental tests are widely used in cardiopulmonary exercise testing, both in the clinical domain and in sport sciences. The highest workload (denoted Wpeak) reached in the test is key information for assessing the individual body response to the test and for analyzing possible cardiac failures and planning rehabilitation, and training sessions. Being physically very demanding, incremental tests can significantly increase the body stress on monitored individuals and may cause cardiopulmonary overload. This article presents a new approach to cardiopulmonary testing that addresses these drawbacks. During the test, our approach analyzes the individual body response to the exercise and predicts the Wpeakvalue that will be reached in the test and an evaluation of its accuracy. When the accuracy of the prediction becomes satisfactory, the test can be prematurely stopped, thus avoiding its entire execution. To predict Wpeak, we introduce a new index, the CardioPulmonary Efficiency Index (CPE), summarizing the cardiopulmonary response of the individual to the test. Our approach analyzes the CPE trend during the test, together with the characteristics of the individual, and predicts Wpeak. A K-nearest-neighbor-based classifier and an ANN-based classier are exploited for the prediction. The experimental evaluation showed that the Wpeakvalue can be predicted with a limited error from the first steps of the test.

Cardiopulmonary exercise testing is an objective method to evaluate both the cardiac and pulmonary functions. It is used in different application domains, ranging from the clinical domain to sport sciences, to assess possible cardiac failures as well as athete performance. The highest workload reached in the test is a key information to evaluate the individual's physiological characteristics, to plan rehabilitation and/or training sessions. However, these tests are physically very demanding and may expose the tested individual to cardiopulmonary overload. This paper presents a new approach that allows an early prediction of the highest workload that will be reached in the cardiopulmonary test. The test can be prematurely stopped, avoiding its entire execution. The proposed approach relies on a new index, the CardioPulmonary Efficiency Index, which describes the cardiopulmonary response of an individual by summarizing the physiological signals monitored during the test. A k-Nearest Neighbor based classifier analyzes the index trend during the test, together with the characteristics of the individual, and predicts the highest workload. Preliminary experiments, performed on a real dataset provided by the CSA Sport Training Center, showed that the proposed approach is able to effectively predict the highest workload with a limited error since the first steps of the test.

Many real-life databases are updated by means of incoming business information. In these databases (e.g., transactional data from large retail chains, call-detail records), the content evolves through periodical insertions (or deletions) of data blocks. Since data evolve over time, algorithms have to be devised to incrementally update data mining models. This paper presents a novel index, called I-Forest, to support itemset mining on incoming data blocks, where new blocks are inserted periodically, or old blocks are discarded. The I-Forest structure provides a complete data representation and allows different kind of analyses (e.g., investigate quarterly data), besides supporting user-defined time and support constraints. The I-Forest index has been implemented into the PostgreSQL open source DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the effectiveness of the I-Forest-based approach to perform itemset mining with both time and support constraints. The execution time of the I-Forest-based itemset mining technique is often faster than the Prefix-Tree algorithm accessing static data on flat files. © 2010 Wiley Periodicals, Inc.

This paper presents the IMine index, a general and compact structure which provides tight integration of item set extraction in a relational DBMS. Since no constraint is enforced during the index creation phase, IMine provides a complete representation of the original database. To reduce the I/O cost, data accessed together during the same extraction phase are clustered on the same disk block. The IMine index structure can be efficiently exploited by different item set extraction algorithms. In particular, IMine data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories. The IMine index has been integrated into the PostgreSQL DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large datasets. Item set mining supported by the IMine index shows performance always comparable with, and sometimes better than, state of the art algorithms accessing data on flat file.

Associative classification is a promising technique to build accurate classifiers. However, in large or correlated datasets, association rule mining may yield huge rule sets. Hence, several pruning techniques have been proposed to select a small subset of high quality rules. We argue that rule pruning should be reduced to a minimum, since the availability of a "rich" rule set may improve the accuracy of the classifier. The L^3 associative classifier is built by means of a lazy pruning technique which discards exclusively rules that only misclassify training data. Classification of unlabeled data is performed in two steps. A small subset of high quality rules is first considered. When this set is not able to classify the data, a larger rule set is exploited. This second set includes rules usually discarded by previous approaches. To cope with the need of mining large rule sets and efficiently use them for classification, a compact form is proposed to represent a complete rule set in a space-efficient way and without information loss. An extensive experimental evaluation on real and synthetic datasets shows that L^3 improves the classification accuracy with respect to previous approaches.

Many efforts have been devoted to couple data mining activities with relational DBMSs, but a true integration into the relational DBMS kernel has been rarely achieved. This paper presents a novel indexing technique, which represents transactions in a succinct form, appropriate for tightly integrating frequent itemset mining in a relational DBMS. The data representation is complete, i.e., no support threshold is enforced, in order to allow reusing the index for mining itemsets with any support threshold. Furthermore, an appropriate structure of the stored information has been devised, in order to allow a selective access of the index blocks necessary for the current extraction phase. The index has been implemented into the PostgreSQL open source DBMS and exploits its physical level access methods. Experiments have been run for various datasets, characterized by different data distributions. The execution time of the frequent itemset extraction task exploiting the index is always comparable with and sometime faster than a C++ implementation of the FP-growth algorithm accessing data stored on a flat file.

Given a class model built from a dataset including labeled data, classification assigns a new data object to the appropriate class. In associative classification the class model (i.e., the classifier) is a set of association rules. Associative classification is a promising technique for the generation of highly accurate classifiers. In this article, we present a compact form which encodes without information loss the classification knowledge available in a classification rule set. This form includes the rules that are essential for classification purposes, and thus it can replace the complete rule set. The proposed form is particularly effective in dense datasets, where traditional extraction techniques may generate huge rule sets. The reduction in size of the rule set allows decreasing the complexity of both the rule generation step and the rule pruning step. Hence, classification rule extraction can be performed also with low support, in order to extract more, possibly useful, rules.

The emerging field of self-repair computing is expected to have a major impact on deployable systems for space missions and defense applications, where high reliability, availability, and serviceability are needed. In this context, RAM (random access memories) are among the most critical components. This paper proposes a built-in self-repair (BISR) approach for RAM cores. The proposed design, introducing minimal and technology-dependent overheads, can detect and repair a wide range of memory faults including: stuck-at, coupling, and address faults. The test and repair capabilities are used on-line, and are completely transparent to the external user, who can use the memory without any change in the memory-access protocol. Using a fault-injection environment that can emulate the occurrence of faults inside the module, the effectiveness of the proposed architecture in terms of both fault detection and repairing capability was verified. Memories of various sizes have been considered to evaluate the area-overhead introduced by this proposed architecture.

The Functional BIST approach is a rather new BIST technique based on exploiting embedded system functionality to generate deterministic test patterns during BIST. The approach takes advantages of two well-known testing techniques, the arithmetic BIST approach and the reseeding method. The main contribution of the present paper consists in formulating the problem of an optimal reseeding computation as an instance of the set covering problem. The proposed approach guarantees high flexibility, is applicable to different functional modules, and, in general, provides a more efficient test set encoding then previous techniques. In addition, the approach shorts the computation time and allows to better exploiting the tradeoff between area overhead and global test length as well as to deal with larger circuits.

Presents a tool to achieve proper reliability levels in systems based on memories, allowing the automatic insertion of BIST architectures for both OFF-line and ON-line memory testing. While OFF-line memory testing was partially targeted by the available commercial tools, ON-line memory testing has so far not been covered. The set of algorithms and architectures supported by the tool is not limited, and it can be easily extended to include innovative architectures and achieve the reliability requirements in any application. Using the tool, the designer can generate dependable memories, trading-off in the design process dependability properties and costs.

Functional BIST is a promising solution for self-testing complex digital systems at reduced costs in terms of area and performance degradation. The present paper addresses the computation of optimal seeds for an arbitrary sequential module to be used as a hardware test pattern generator. Up to now, only linear feedback shift registers and accumulator based structures have been used for deterministic test pattern generation by reseeding. In this paper, a method is proposed which can be applied to general finite state machines. Nevertheless the method is absolutely general, for sake of comparison with previous approaches, in this paper an accumulator based unit is assumed as pattern generator module. Experiments prove the effectiveness of the approach which outperforms previous results for accumulators, in terms of test size and test time, without sacrificing the fault detection capability.

The paper proposes a C/C++ source-to-source compiler able to increase the dependability properties of a given application. The adopted strategies are based on two main techniques, code re-ordering and variables duplication. The proposed approach is portable to any platform, it can be applied to any C/C++ source code, and it introduces code modifications that are transparent to the original program functionality. The RECCO tool, which fully automates the process, allows the user to trade-off between the level of dependability improvement and the performance degradation due to the code modification.

This paper presents the integration of a proprietary hierarchical and distributed test access mechanism called HD/sup 2/BIST and a BIST insertion commercial tool. The paper briefly describes the architecture and the features of both the environments and it presents some experimental results obtained on an industrial SoC.

Proposes HD/sup 2/BIST, a complete hierarchical framework for BIST scheduling, data patterns delivering, and diagnosis of a complex system including embedded cores with different test requirements as full scan cores, partial scan cores, or BIST-ready cores. The main goal of HD/sup 2/BIST is to maximize and simplify the reuse of the built-in test architectures, giving the chip designer the highest flexibility in planning the overall SoC test strategy. HD/sup 2/BIST defines a test access method able to provide a direct "virtual" access to each core of the system, and can be conceptually considered as a powerful complement to the P1500 standard, whose main target is to make the test interface of each core independent from the vendor.

In this paper we present DSDK/sup 1/, a Dependable Software Development Kit designed to support the software engineer in releasing dependable applications for embedded systems, starting from either commercial or native software. The proposed kit integrates four different tools: RECCO, a source-to-source compiler able to transform any C/C++ native source code in a more dependable version of the same code, WRAP a tool able to wrap executable software modules and transparently enhance their dependability characteristic, EXEM, an external world and device emulator fool, and BOND, a powerful Fault Injection environment able to emulate the occurrence of different types of faults in the system and to evaluate the dependability properties of the overall system. Experimental results gathered on an embedded system running Embedded NT OS demonstrate the effectiveness and the flexibility of the tool.

The term "functional BIST" describes a test method to control functional modules so that they generate a deterministic test set, which targets structural faults within other parts of the system. It is a promising solution for self-testing complex digital systems at reduced costs in terms of area overhead and performance degradation. While previous work mainly investigated the use of functional modules for generating pseudo-random and pseudo-exhaustive test patterns, the present paper shows that a variety of modules can also be used as a deterministic test pattern generator via an appropriate reseeding strategy. This method enables a BIST technique that does not introduce additional hardware like test points and test registers into combinational and pipelined modules under test. The experimental results prove that the reseeding method works for accumulator based structures, multipliers, or encryption modules as efficiently as for the classic linear feedback shift registers, and some times even better.

This paper presents a new integrated approach to logic optimization for sequential circuits. The approach is based on the redundancy addition and removal algorithm, which is based on automatic test pattern generation (ATPG) techniques, and improves it using symbolic techniques based on BDDs. The advantage of the integrated approach lies in the ability of Symbolic Techniques to provide exact and extensive information about the sequential behavior of the portion of the circuit that is of interest to the logic optimization algorithm. Experimental results are provided that show the superiority of the approach to the original ATPG-based optimization approach.

This paper proposes HD-BIST, a complete framework to support the definition of the scheduling strategy and mechanism of the BISTed blocks of a complex system. Three different layers are presented, to define the HD-BIST approach in terms of a set of high-level BIST scheduling primitives, a communication protocol, and a possible hardware implementation, respectively.

This paper presents the test strategy adopted at different hierarchical abstraction levels (from board to die level) during the development of a multichannel data acquisition and signal processing MCM, designed for the new generation experiments of high-energy physics on the Large Hadron Collider accelerator at CERN.

In digital systems design, strict reliability constraints usually impose very low fault latency and high degree of fault detection of permanent and transient faults. In particular, memory modules, as either devices or IP cores, appeared as one of the most critical parts. This paper presents an advanced on-line memory BIST architecture implemented as an IP core developed for telecommunication applications. A fault latency reduction architecture, a code-based fault detection scheme, and an architecture-based fault avoidance have been composed to meet the required reliability constraints.

High-level test pattern generation is today a widely investigated research topic. The present paper proposes a fully automated, simulation-based ATPG system, to address test pattern generation for circuits described at the RT-level. The approach is based on a set of suitable testability metrics, and the test pattern generation phase resorts to genetic algorithms. Experiments show the excellent fault coverage provided by the RT-level test patterns, when applied at the final gate-level. The approach, being based on a high-level representation, promises to be particularly suited where gate-level ATPGs are often inefficient, mainly for large circuits and for control-intensive designs.

This paper aims at broadening the scope of hierarchical ATPG to the behavioral-level. The main problem of using behavioral information for ATPG is the mismatch of timing models between the behavioral- and gate-levels. Theoretical analysis shows that the definition of the concept of dominated sequences captures the needed link between the levels. To validate the concept correctness, and to show how it can be profitably exploited, a prototypical hierarchical ATPG is presented, and experimental results show that the performance of a simple ATPG algorithm that is able to exploit behavioral information is much higher than a much more sophisticated gate-level tool.

This paper considers the testability implications of low power design methodologies. Low power and high testability are shown to be highly contrasting requirements, and an optimization algorithm is proposed, which is able to explore the trade-off between them. The algorithm is based on a newly proposed power estimation function, and on an estimate of the expected rest length of a pseudo-random rest session. Given these estimates a Genetic Algorithm, exploiting some symbolic computations with BDDs, provides a state reencoding for the circuit. The algorithm is experimental shown both to provide good results from the power optimization point of view, and to be able to sacrifice, on the designer's request, some of the power and area optimization in favor of testability improvement.

This paper addresses the issue of identifying a Cellular Automaton able to generate deterministic input patterns to detect stuck-at faults inside an FSM. A suitable hardware structure is first identified. An evolutionary algorithm is then proposed, which directly identifies a Cellular Automaton able to reach a very good Fault Coverage. The novelty of the method consists in combining the generation of test patterns with the synthesis of a Cellular Automaton able to reproduce them. Experimental results are provided, which show that the proposed hardware architecture and algorithmic approach outperform more traditional solutions, based on ATPG tools and FSM synthesis, from the point of view of both applicability and area occupation, while reaching the same Fault Coverage.