The recently introduced streaming table concept, a fully native representation of streaming data inside a DBMS, enabled modern data-intensive applications with one-time queries (OTQs) and continuous queries (CQs) capabilities on both streaming and standard relational tables. In this paper, we fully acknowledge the temporal nature of streaming tables and we propose to go one step further and integrate them in a temporal DBMS context, where time management is native. Our aim is to break the traditional barrier between the streaming and the temporal worlds, offering complete interoperability between streams and temporal data. To this end, we present a continuous temporal algebra supporting both OTQs and CQs seamlessly on streaming, standard and temporal relational tables. We further show how the transition from continuous to one-time semantics can be managed by defining suitable translation rules, which can also be used as a basis for the implementation of the proposed continuous algebra in a temporal DBMS.

Data stream management systems (DSMSs) are conceived for running continuous queries (CQs) on the most recently streamed data. This model does not completely fit the needs of several modern data-intensive applications that require to manage recent/historical/static data and execute both CQs and OTQs joining such data. In order to cope with these new needs, some DSMSs have moved toward the integration of database management systems (DBMSs) functionalities to augment their capabilities. In this paper we adopt the opposite perspective and we lay the groundwork for extending DBMSs to natively support streaming facilities. To this end, we introduce a new kind of table, the streaming table, as a persistent structure where streaming data enters and remains stored for a long period, ideally forever. Streaming tables feature a novel access paradigm: continuous writes and one-time as well as continuous reads. We present a streaming table implementation and two novel types of indices that efficiently support both update and scan high rates. A detailed experimental evaluation shows the effectiveness of the proposed technology.

In this paper we investigate the employment of different data reduction techniques to minimize V2I communication in an Intelligent Transportation System (ITS). We consider the context of the PEGASUS Project, where vehicles are equipped with sensor-based devices able to compute and communicate to a Control Centre (CC) information like vehicleăĂŹs position and speed. The CC relies on a general-purpose data management module that supports the execution of continuous queries as well as standard SQL one-time queries on the collected data to provide various infomobility services.

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities. Traditional business intelligence systems do not provide support to this end. Peer Data Management Systems (PDMSs) have been proposed as architectures to support sharing of operational data across networks of peers while guaranteeing peers' autonomy, based on semantic mappings that mediate between the heterogeneous schemata exposed by peers. In line with the PDMS infrastructure, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs be properly reformulated on the other peers. In this direction, we present a language for the definition of mappings between the multidimensional schemata of peers, and we introduce a query reformulation framework that relies on the translation of these mappings towards relational schemata. Finally, we sketch the query reformulation algorithm by outlining the reformulation steps of typical OLAP queries.

The largeness and the heterogeneity of most graph-modeled datasets in several database application areas make the query process a real challenge because of the lack of a complete knowledge of the vocabulary used, as well as of the information about the structural relationships between the data.

Peer Data Management Systems (PDMSs) have been introduced as a solution to the problem of large-scale sharing of semantically rich data. A PDMS consists of semantic peers connected through semantic mappings. Querying a PDMS may lead to very poor results, because of the semantic degradation due to the approximations given by the traversal of the semantic mappings, thus leading to the problem of how to boost a network of mappings in a PDMS.In this paper we propose a strategy for the incremental maintenance of a flexible network organization that clusters together peers which are semantically related in Semantic Overlay Networks (SONs), while maintaining a high degree of node autonomy. Semantic features, a summarized representation of clusters, are stored in a light structure which effectively assists a newly entering peer when choosing its semantically closest overlay networks. Then, each peer is supported in the selection of its own neighbors within each overlay network according to two policies: Range-based selection and k-NN selection. For both policies, we introduce specific algorithms which exploit a distributed indexing mechanism for efficient network navigation. The proposed approach has been implemented in a prototype where its effectiveness and efficiency have been extensively tested.

In recent years, the availability of complex data repositories (e.g., multimedia, genomic, semistructured databases) has paved the way to new potentials as to data querying. In this scenario, similarity and fuzzy techniques have proven to be successful principles for effective data retrieval. However, most proposals are domain specific and lack of a general and integrated approach to deal with generalized complex queries, i.e., queries where multiple conditions are expressed, possibly on complex as well as on traditional data. To overcome such limitations, much work has been devoted to the development of middleware systems to support query processing on multiple repositories. On a similar line, We present a formal framework to permeate complex similarity and fuzzy queries within a relational database system. As an example, we focus on multimedia data, which is represented in an integrated view with common database data. We have designed an application layer that relies on an algebraic query language, extended with MM-tailored operators, and that maps complex similarity and fuzzy queries to standard SQL statements that can be processed by a relational database system, exploiting standard facilities of modern extensible RDBMS. To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries to complex queries gathering a mixture of Boolean, similarity, and fuzzy predicates on the data.

The use of formal methods early in the development process has been advocated as a way of improving the quality of software products and their production process. Here we study the influence of a formal requirements document on the next phase in the software process, that is design. We suggest that formal design should coherently follow from formal requirements. We show that two different formal notations can be effectively used, one for writing requirements specification and one for design specification. We also consider how a design specification can be formally checked with respect to requirements specification. The notations we choose are well known: the Z notation for requirements and the Larch two-tiered language for design. We show how a number of tools based on these notations can be used to improve the quality of the documents produced during the development process.