{"title":"Automatic Rule Generation for Constraint Enforcement in Active Databases","authors":"S. Ceri, P. Fraternali, S. Paraboschi, L. Tanca","doi":"10.1007/978-1-4471-3554-8_10","DOIUrl":"https://doi.org/10.1007/978-1-4471-3554-8_10","url":null,"abstract":"","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133961361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Cuzzocrea, F. Furfaro, E. Masciari, C. Sirangelo
{"title":"Approximate Query Answering on Sensor Network Data Streams","authors":"A. Cuzzocrea, F. Furfaro, E. Masciari, C. Sirangelo","doi":"10.1201/9780203356869.ch4","DOIUrl":"https://doi.org/10.1201/9780203356869.ch4","url":null,"abstract":"Sensor networks represent a non traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Traditional DBMSs, which are based on an exact and detailed representation of information, are not suitable in this context, as all the information carried by a data stream cannot be stored within a bounded storage space. Thus, compressing data (by possibly loosing less relevant information) and storing their compressed representation, rather than the original one, becomes mandatory. This approach aims to store as much information carried by the stream as possible, but makes it unfeasible to provide exact answers to queries on the stream content. However, exact answers to queries are often not necessary, as approximate ones usually suffice to get useful reports on the world monitored by the sensors. In this paper we propose a technique for providing fast approximate answers to aggregate queries on sensor data streams. Our proposal is based on a hierarchical summarization of the data stream embedded into a flexible indexing structure, which permits us to both access and update compressed data efficiently. The compressed representation of data is updated continuously, as new sensor readings arrive. When the available storage space is not enough to store new data, some space is released by compressing the “oldest” stored data progressively, so that recent information (which is usually the most relevant to retrieve) is represented with more detail than old one.","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Big Data Analytics","authors":"Michelangelo Ceci","doi":"10.1007/978-3-319-77525-8_100026","DOIUrl":"https://doi.org/10.1007/978-3-319-77525-8_100026","url":null,"abstract":"","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123774455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Probabilistic Hierarchical Approach for Pattern Discovery in Collaborative Filtering Data (Extended Abstract)","authors":"Nicola Barbieri, G. Manco, E. Ritacco","doi":"10.1137/1.9781611972818.54","DOIUrl":"https://doi.org/10.1137/1.9781611972818.54","url":null,"abstract":"This paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being ‘universally appreciated’) and local patterns ( tendency of users within a community to express a common preference on the same group of items). We reformulate the collaborative filtering approach as a clustering problem in a high-dimensional setting, and propose a probabilistic approach to model the data. The core of our approach is a co-clustering strategy, arranged in a hierarchical fashion: first, user communities are discovered, and then the information provided by each user community is used to discover topics, grouping items into categories. The resulting probabilistic framework can be used for detecting interesting relationships between users and items within user communities. The experimental evaluation shows that the proposed model achieves a competitive prediction accuracy with respect to the state-of-art collaborative filtering approaches.","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"725 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116131525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natalia Antonioli, F. Castanò, Spartaco Coletta, Stefano Grossi, D. Lembo, M. Lenzerini, Antonella Poggi, Emanuela Virardi, Patrizia Castracane
{"title":"Developing Ontology-based Data Management for the Italian Public Debt","authors":"Natalia Antonioli, F. Castanò, Spartaco Coletta, Stefano Grossi, D. Lembo, M. Lenzerini, Antonella Poggi, Emanuela Virardi, Patrizia Castracane","doi":"10.3233/978-1-61499-438-1-372","DOIUrl":"https://doi.org/10.3233/978-1-61499-438-1-372","url":null,"abstract":"In this paper we present an ontology-based data management (OBDM) project concerning the Italian public debt domain, carried out within a joint collaboration between Sapienza University of Rome and the Department of Treasury of the Italian Ministry of Economy and Finance. We discuss the motivations at the basis of this project and present the main characteristics of the ontology we have built. We also describe the mechanisms we used to link the ontology to the actual data and the tools we have adopted for supporting ontology development and maintenance, as well as exploiting OBDM services. Finally, we provide a thorough evaluation of the ontology we produced and discuss in detail the role that it plays within the whole information system of the ministry department responsible for managing Italian public debt data.","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116210533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clustered Majority Judgement","authors":"Emanuele d'Ajello, Davide Formica, E. Masciari, Gaia Mattia, Arianna Anniciello, Cristina Moscariello, Stefano Quintarelli, Davide Zaccarella","doi":"10.5220/0011319400003269","DOIUrl":"https://doi.org/10.5220/0011319400003269","url":null,"abstract":"In order to overcome the classical methods of judgement, in the literature there is a lot of material about different methodology and their intrinsic limitations. One of the most relevant modern model to deal with votation system dynamics is the Majority Judgement. It was created with the aim of reducing polarization of the electorate in modern democracies and not to alienate minorities, thanks to its use of a highest median rule, producing more informative results than the existing alternatives. Nonetheless, as shown in the literature, in the case of multiwinner elections it can lead to scenarios in which minorities, albeit numerous, are not adequately represented. For this reason our aim is to implement a clustered version of this algorithm, in order to mitigate these disadvantages: it creates clusters taking into account the similarity between the expressed judgements and then for, each of these created groups, Majority Judgement rule is applied to return a ranking over the set of candidates. These traits make the algorithm available for applications in different areas of interest in which a decisional process is involved.","PeriodicalId":312822,"journal":{"name":"Sistemi Evoluti per Basi di Dati","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}