Selected Journal papers (co)authored by Giuseppe Manco

[1] Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. Efficient methods for influence-based network-oblivious community detection. ACM Trans. Intell. Syst. Technol., 8(2):32:1-32:31, December 2016. [ bib | DOI | .pdf ] Code ]
The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple We study the problem of detecting social communities when the social graph is not available but instead we have access to a log of user activity, that is, a dataset of tuples (u, i, t) recording the fact that user u “adopted” item i at time t. We propose a stochastic framework that assumes that the adoption of items is governed by an underlying diffusion process over the unobserved social network and that such a diffusion model is based on community-level influence. That is, we aim at modeling communities through the lenses of social contagion. By fitting the model parameters to the user activity log, we learn the community membership and the level of influence of each user in each community. The general framework is instantiated with two different diffusion models, one with discrete time and one with continuous time, and we show that the computational complexity of both approaches is linear in the number of users and in the size of the propagation log. Experiments on synthetic data with planted community structure show that our methods outperform non-trivial baselines. The effectiveness of the proposed techniques is further validated on real-word data, on which our methods are able to detect high-quality communities.

Keywords: Social influence, information diffusion, network-oblivious community detection, social network analysis
[2] Giuseppe Manco, Pasquale Rullo, Lorenzo Gallucci, and Mirko Paturzo. Rialto: A knowledge discovery suite for data analysis. Expert Syst. Appl., 59(C):145-164, October 2016. [ bib | DOI | .pdf ]
A Knowledge Discovery (KD) process is a complex inter-disciplinary task, where di↵erent types of techniques coexist and cooperate for the purpose of extract- ing useful knowledge from large amounts of data. So, it is desirable having a unifying environment, built on a formal basis, where to design and perform the overall process. In this paper we propose a general framework which for- malizes a KD process as an algebraic expression, that is, as a composition of operators representing elementary operations on two worlds: the data and the model worlds. Then, we describe a KD platform, named Rialto, based on such a framework. In particular, we provide the design principles of the underlying architecture, highlight the basic features, and provide a number of experimental results aimed at assessing the e↵ectiveness of the design choices.

Keywords: Business analytics platforms, Data mining, Knowledge Discovery process
[3] Shirley Coleman, Rainer Göb, Giuseppe Manco, Antonio Pievatolo, Xavier Tort-Martorell, and Marco Seabra Reis. How can smes benefit from big data? challenges and a path forward. Quality and Reliability Engineering International, 32(6):2151-2164, 2016. QRE-15-0533.R1. [ bib | DOI | http ]
Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success.

Keywords: predictive analytics, maturity model, data science, skills shortage
[4] Fabrizio Angiulli, Fabio Fassetti, Giuseppe Manco, Luigi Palopoli. Outlying property detection with numerical attributes. Data Mining and Knowledge Discovery, pages 1-30, 2016. [ bib | DOI | .pdf | Code ]
The outlying property detection problem (OPDP) is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. This problem has been recently analyzed focusing on categorical attributes only. However, numerical attributes are very relevant and widely used in databases. Therefore, in this paper, we analyze the OPDP within a context where also numerical attributes are taken into account, which represents a relevant case left open in the literature. As major contributions, we present an efficient parameter-free algorithm to compute the measure of object exceptionality we introduce, and propose a unified framework for mining exceptional properties in the presence of both categorical and numerical attributes.

Keywords: Outlier detection Outlying properties Kernel density estimation Clustering
[5] Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. Topic-aware social influence propagation models. Knowledge and Information Systems, pages 1-30, 2013. [ bib | DOI | .pdf | Code ]
The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. However, these propagation models have a very large number of parameters which could lead to overfitting. Therefore, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. Instead of considering user-to-user influence, the proposed model focuses on user authoritativeness and interests in a topic, leading to a drastic reduction in the number of parameters of the model. We devise methods to learn the parameters of the models from a data set of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.

Keywords: Social influence; Topic modeling; Topic-aware propagation model; Viral marketing
[6] Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, and Antonio Bevacqua. Probabilistic topic models for sequence data. Machine Learning, 93(1):5-29, 2013. [ bib | DOI | .pdf | slides | Code ]
Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting three different conditional Markovian assumptions: (i) the token generation depends on the current topic and on the previous token; (ii) the topic associated with each observation depends on topic associated with the previous one; (iii) the token generation depends on the current and previous topic. For each of these modeling assumptions we present a Gibbs Sampling procedure for parameter estimation. Experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the sequence-modeling approaches.

[7] Gianni Costa, Giuseppe Manco, Riccardo Ortale, and Ettore Ritacco. Hierarchical clustering of xml documents focused on structural components. Data & Knowledge Engineering, 84(0):26 - 46, 2013. [ bib | DOI | .pdf ]
Clustering XML documents by structure is the task of grouping them by common structural components. Hitherto, this has been accomplished by looking at the occurrence of one preestablished type of structural components in the structures of the XML documents. However, the a-priori chosen structural components may not be the most appropriate for effective clustering. Moreover, it is likely that the resulting clusters exhibit a certain extent of inner structural inhomogeneity, because of uncaught differences in the structures of the XML documents, due to further neglected forms of structural components. To overcome these limitations, a new hierarchical approach is proposed, that allows to consider (if necessary) multiple forms of structural components to isolate structurally-homogeneous clusters of XML documents. At each level of the resulting hierarchy, clusters are divided by considering some type of structural components (unaddressed at the preceding levels), that still differentiate the structures of the XML documents. Each cluster in the hierarchy is summarized through a novel technique, that provides a clear and differentiated understanding of its structural properties. A comparative evaluation over both real and synthetic XML data proves that the devised approach outperforms established competitors in effectiveness and scalability. Cluster summarization is also shown to be very representative.

[8] Gianni Costa, Giuseppe Manco, Riccardo Ortale, and Ettore Ritacco. From global to local and viceversa: uses of associative rule learning for classification in imprecise environments. Knowl. Inf. Syst., 33(1):137-169, 2011. [ bib | .pdf ]
We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model implements a global-to-local scheme, where the response of a global rule-based classifier is refined by performing a probabilistic analysis of the coverage of its rules. In particular, the coverage of the individual rules is used to learn local probabilistic models, which ultimately refine the predictions from the corresponding rules of the global classifier. The second model implements a dual local-to-global strategy, in which single classification rules are combined within an exponential probabilistic model in order to boost the overall performance as a side effect of mutual influence. Several variants of the basic ideas are studied, and their perfor- mances are thoroughly evaluated and compared with state-of-the-art algorithms on standard benchmark datasets.

[9] Gianni Costa, Giuseppe Manco, and Riccardo Ortale. An incremental clustering scheme for data de-duplication. Data Min. Knowl. Discov., 20(1):152-187, 2010. [ bib | http ]
We propose an incremental technique for discovering duplicates in large databases of textual sequences, i.e., syntactically different tuples, that refer to the same real-world entity. The problem is approached from a clustering perspective: given a set of tuples, the objective is to partition them into groups of duplicate tuples. Each newly arrived tuple is assigned to an appropriate cluster via nearest-neighbor classifi- cation. This is achieved by means of a suitable hash-based index, that maps any tuple to a set of indexing keys and assigns tuples with high syntactic similarity to the same buckets. Hence, the neighbors of a query tuple can be efficiently identified by simply retrieving those tuples that appear in the same buckets associated to the query tuple itself, without completely scanning the original database. Two alternative schemes for computing indexing keys are discussed and compared. An extensive experimental evaluation on both synthetic and real data shows the effectiveness of our approach.

[10] Eugenio Cesario, Francesco Folino, Antonio Locane, Giuseppe Manco, and Riccardo Ortale. Boosting text segmentation via progressive classification. Knowl. Inf. Syst., 15(3):285-320, 2008. [ bib | http ]
A novel approach for reconciling tuples stored as free text into an existing attribute schema is proposed. The basic idea is to subject the available text to progressive classification, i.e., a multi-stage classification scheme where, at each intermediate stage, a classifier is learnt that analyzes the textual fragments not reconciled at the end of the previous steps. Classifica- tion is accomplished by an ad hoc exploitation of traditional association mining algorithms, and is supported by a data transformation scheme which takes advantage of domain-specific dictionaries/ontologies. A key feature is the capability of progressively enriching the avail- able ontology with the results of the previous stages of classification, thus significantly improving the overall classification accuracy. An extensive experimental evaluation shows the effectiveness of our approach.

[11] Giuseppe Manco, Elio Masciari, and Andrea Tagarelli. Mining categories for emails via clustering and pattern discovery. J. Intell. Inf. Syst., 30(2):153-181, 2008. [ bib | http ]
The continuous exchange of information by means of the popular email service has raised the problem of managing the huge amounts of messages received from users in an effective and efficient way. We deal with the problem of email classification by conceiving suitable strategies for: (1) organizing messages into homogeneous groups, (2) redirecting further incoming messages according to an initial organization, and (3) building reliable descriptions of the message groups discovered. We propose a unified framework for handling and classifying email messages. In our framework, messages sharing similar features are clustered in a folder organization. Clustering and pattern discovery techniques for mining struc- tured and unstructured information from email messages are the basis of an overall process of folder creation/maintenance and email redirection. Pattern discovery is also exploited for generating suitable cluster descriptions that play a leading role in cluster updating. Experimental evaluation performed on several personal mailboxes shows the effectiveness of our approach.

[12] Sergio Flesca, Giuseppe Manco, Elio Masciari, Luigi Pontieri, and Andrea Pugliese. Exploiting structural similarity for effective web information extraction. Data Knowl. Eng., 60(1):222-234, 2007. [ bib | http ]
In this paper, we propose a classification technique for Web pages, based on the detection of structural similarities among semistructured documents, and devise an architecture exploiting such technique for the purpose of information extraction. The proposal significantly differs from standard methods based on graph-matching algorithms, and is based on the idea of representing the structure of a document as a time series in which each occurrence of a tag corresponds to an impulse. The degree of similarity between documents is then stated by analyzing the frequencies of the corresponding Fourier transform. Experiments on real data show the effectiveness of the proposed technique.

[13] Eugenio Cesario, Giuseppe Manco, and Riccardo Ortale. Top-down parameter-free clustering of high-dimensional categorical data. IEEE Trans. Knowl. Data Eng., 19(12):1607-1624, 2007. [ bib | http ] Code (requires Weka) ] Windows executable ]
A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. In the second phase, the number of clusters is fixed, and an attempt to optimize cluster assignments is done. On the basis of such features, the algorithm attempts to improve the overall quality of the whole partition and finds clusters in the data, whose number is naturally established on the basis of the inherent features of the underlying data set rather than being previously specified. Furthermore, the approach is parametric to the notion of cluster quality: Here, a cluster is defined as a set of tuples exhibiting a sort of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high-dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Experiments on both synthetic and real data prove that the devised algorithm scales linearly and achieves nearly optimal results in terms of compactness and separation.

[14] Gianluigi Greco, Antonella Guzzo, Giuseppe Manco, and Domenico Saccà. Mining unconnected patterns in workflows. Inf. Syst., 32(5):685-712, 2007. [ bib | http ]
General patterns of execution that have been frequently scheduled by a workflow management system provide the administrator with previously unknown, and potentially useful information, e.g., about the existence of unexpected causalities between subprocesses of a given workflow. This paper investigates the problem of mining unconnected patterns on the basis of some execution traces, i.e., of detecting sets of activities exhibiting no explicit dependency relationships that are frequently executed together. The problem is faced in the paper by proposing and analyzing two algorithms. One algorithm takes into account information about the structure of the control-flow graph only, while the other is a smart refinement where the knowledge of the frequencies of edges and activities in the traces at hand is also accounted for, by means of a sophisticated graphical analysis. Both algorithms have been implemented and integrated into a system prototype, which may profitably support the enactment phase of the workflow. The correctness of the two algorithms is formally proven, and several experiments are reported to evidence the ability of the graphical analysis to significantly improve the performances, by dramatically pruning the search space of candidate patterns.

[15] Sergio Flesca, Giuseppe Manco, Elio Masciari, Luigi Pontieri, and Andrea Pugliese. Fast detection of xml structural similarity. IEEE Trans. Knowl. Data Eng., 17(2):160-175, 2005. [ bib | http ]
Because of the widespread diffusion of semistructured data in XML format, much research effort is currently devoted to support the storage and retrieval of large collections of such documents. XML documents can be compared as to their structural similarity, in order to group them into clusters so that different storage, retrieval, and processing techniques can be effectively exploited. In this scenario, an efficient and effective similarity function is the key of a successful data management process. We present an approach for detecting structural similarity between XML documents which significantly differs from standard methods based on graph-matching algorithms, and allows a significant reduction of the required computation costs. Our proposal roughly consists of linearizing the structure of each XML document, by representing it as a numerical sequence and, then, comparing such sequences through the analysis of their frequencies. First, some basic strategies for encoding a document are proposed, which can focus on diverse structural facets. Moreover, the theory of Discrete Fourier Transform is exploited to effectively and efficiently compare the encoded documents (i.e., signals) in the domain of frequencies. Experimental results reveal the effectiveness of the approach, also in comparison with standard methods.

[16] Gianluigi Greco, Antonella Guzzo, Giuseppe Manco, and Domenico Saccà. Mining and reasoning on workflows. IEEE Trans. Knowl. Data Eng., 17(4):519-534, 2005. [ bib | http ]
Today's workflow management systems represent a key technological infrastructure for advanced applications that is attracting a growing body of research, mainly focused in developing tools for workflow management, that allow users both to specify the “static” aspects, like preconditions, precedences among activities, and rules for exception handling, and to control its execution by scheduling the activities on the available resources. This paper deals with an aspect of workflows which has so far not received much attention even though it is crucial for the forthcoming scenarios of large scale applications on the Web: Providing facilities for the human system administrator for identifying the choices performed more frequently in the past that had lead to a desired final configuration. In this context, we formalize the problem of discovering the most frequent patterns of executions, i.e., the workflow substructures that have been scheduled more frequently by the system. We attacked the problem by developing two data mining algorithms on the basis of an intuitive and original graph formalization of a workflow schema and its occurrences. The model is used both to prove some intractability results that strongly motivate the use of data mining techniques and to derive interesting structural properties for reducing the search space for frequent patterns. Indeed, the experiments we have carried out show that our algorithms outperform standard data mining algorithms adapted to discover frequent patterns of workflow executions.

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