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Chapter and Conference Paper
DQNC2S: DQN-Based Cross-Stream Crisis Event Summarizer
Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve &Re-ranking strategies suffer from the inherent redundancy of multi-stream data and limited s...
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Article
Effective video hyperlinking by means of enriched feature sets and monomodal query combinations
Video content has been increasing at an unprecedented rate in recent years, bringing the need for improved tools providing efficient access to specific contents of interest. Within the management of video cont...
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Article
Training ensembles of faceted classification models for quantitative stock trading
Forecasting the stock markets is among the most popular research challenges in finance. Several quantitative trading systems based on supervised machine learning approaches have been presented in literature. R...
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Article
Discovering cross-topic collaborations among researchers by exploiting weighted association rules
Identifying the most relevant scientific publications on a given topic is a well-known research problem. The Author-Topic Model (ATM) is a generative model that represents the relationships between research to...
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Article
Open AccessScaling associative classification for very large datasets
Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datase...
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Chapter and Conference Paper
Discovering High-Utility Itemsets at Multiple Abstraction Levels
High-Utility Itemset Mining (HUIM) is a relevant data mining task. The goal is to discover recurrent combinations of items characterized by high profit from transactional datasets. HUIM has a wide range of app...
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Article
Predicting critical conditions in bicycle sharing systems
Bicycle sharing systems are eco-friendly transportation systems that have found wide application in Smart urban environments. Monitoring and analyzing the occupancy levels of the system’s stations is crucial f...
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Article
Characterization and search of web services through intensional knowledge
Web service technologies are widely adopted to access services and compose new applications starting from software components available from the shelf. Consequently, more and more service descriptions are beco...
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Chapter and Conference Paper
BAC: A Bagged Associative Classifier for Big Data Frameworks
Big Data frameworks allow powerful distributed computations extending the results achievable on a single machine. In this work, we present a novel distributed associative classifier, named BAC, based on ensemb...
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Chapter and Conference Paper
Reducing Big Data by Means of Context-Aware Tailoring
Context-aware personalization is one of the possible ways to face the problem of information overload, that is, the difficulty of understanding an issue and making decisions when receiving too much infor...
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Chapter and Conference Paper
A Review of Scalable Approaches for Frequent Itemset Mining
Frequent Itemset Mining is a popular data mining task with the aim of discovering frequently co-occurring items and, hence, correlations, hidden in data. Many attempts to apply this family of techniques to Big...
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Chapter and Conference Paper
Hadoop on a Low-Budget General Purpose HPC Cluster in Academia
In the last decade, we witnessed an increasing interest in High Performance Computing (HPC) infrastructures, which play an important role in both academic and industrial research projects. At the same time, du...
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Chapter
Temporal Pattern Mining for Medical Applications
Due to the increased availability of information systems in hospitals and health care institutions, there has been a huge production of electronic medical data, which often contains time information. Temporal ...
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Article
CAS-Mine: providing personalized services in context-aware applications by means of generalized rules
Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allow service providers to adapt their services to actu...
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Chapter and Conference Paper
Semantic-Enriched Data Mining Techniques for Intensional Service Representation
The adoption of Web service technologies to enable collaboration in distributed environments has been made possible by the availability of huge amount of service repositories, that, if not properly controlled,...
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Chapter and Conference Paper
Context-Aware User and Service Profiling by Means of Generalized Association Rules
Context-aware applications allow service providers to adapt their services to actual user needs, by offering them personalized services depending on their current application context. Hence, service providers ...
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Chapter and Conference Paper
Summarizing XML Data by Means of Association Rules
XML is a rather verbose representation of semistructured data, which may require huge amounts of storage space. We propose several summarized representations of XML data, which can both provide succinct inform...
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Chapter and Conference Paper
Majority Classification by Means of Association Rules
Associative classification is a well-known technique for structured data classification. Most previous work on associative classification based the assignment of the class label on a single classification rule...
