Search

Many Natural Language Processing (NLP) tasks, like question answering or analyzing verbatim comments, have started to use word embeddings due to their ability to capture semantic relations between words. Recently, embeddings have been also applied in the geospatial context to represent geospatial ontologies, thanks to their ability to capture semantic similarity. In this paper, we present an analysis of a promising embedding technique particularly suitable for representing hierarchical structures. We conduct a deep technical evaluation of many parameters and their impact on the quality of the representation.

In this paper, we present our experience of using the Team Based Learning (TBL) methodology and the Sonic Pi language in an introductory programming course. Sonic Pi is a code-based musical creation and performance tool, also widely used for computer science (CS) educational purposes, since every musical concept corresponds to a notion of computer programming. This aspect makes it extremely effective in adapting to different learning objectives and environments, and in tuning the topics with learners interests and attitudes. Specifically, we designed two TBL activities aimed at first-year undergraduate students in CS, the first focusing on anticipating a new advanced CS topic and the second on a learning objective of the Introduction to Programming Course.

The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts representation) on the suggestion of relevant types of information to search for. We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space. A comparative analysis of the performance of a query expansion model, using three spatial ontologies defined at different semantic granularity levels, reveals that a fine-grained representation enhances recall. However, precision depends on how closely the ontologies match the way people conceptualize and verbally describe the geographic space.

Nowadays word embeddings are used for many natural language processing (NLP) tasks thanks to their ability of capturing the semantic relations between words. Word embeddings have been mostly used to solve traditional NLP problems, such as question answering, textual entailment and sentiment analysis. This work proposes a new way of thinking about word embeddings that exploits them in order to represent geographical knowledge (e.g., geographical ontologies). We also propose metrics for evaluating the effectiveness of an embedding with respect to the ontological structure on which it is created both in an absolute way and with reference to its application within geolocation algorithms.

We present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of update primitives of XQuery Update. The key feature of the method is the use of an automatic inference algorithm for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as a conformance test w.r.t. the type extracted from the target schema S ' .

From the late 1970s, the database research community has devoted huge efforts in devising more and more effective and efficient solutions for processing queries against data. In this chapter, we retrace some of challenges that have been faced in the last 25 years to enable data to be effectively and efficiently queried and how the notions of queries and query processing themselves have evolved during these five lusters.

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.

Schemas are often used to constrain the content and structure of XML documents. They can be quite big and complex and, thus, difficult to be accessed manually. The ability to query a single schema, a collection of schemas or to retrieve schema components that meet certain structural constraints significantly eases schema management and is, thus, useful in many contexts. In this paper, we propose a query language, named XSPath, specifically tailored for XML schema that works on logical graph-based representations of schemas, on which it enables the navigation, and allows the selection of nodes. We also propose XPath/XQuery-based translations that can be exploited for the evaluation of XSPath queries. An extensive evaluation of the usability and efficiency of the proposed approach is finally presented within the EXup system [9].

Research on database and information system technologies has been rapidly evolving over the last few years. Advances concern either new data types, new management issues, and new kind of architectures and systems. The 17th East-European Conference on Advances in Databases and Information Systems (ADBIS 2013), held on September 1–4, 2013 in Genova, Italy, and associated satellite events aimed at covering some emerging issues concerning such new trends in database and information system research. The aim of this paper is to present such events, their motivations and topics of interest, as well as briefly outline the papers selected for presentations. The selected papers will then be included in the remainder of this volume.

The structure of an XML document can be optionally specified by means of XML Schema, thus enabling the exploitation of structural information for efficient document handling. Upon schema evolution, or when exchanging documents among different collections exploiting related but not identical schemas, the need may arise of adapting a document, known to be valid for a given schema S, to a target schema S'. The adaptation may require knowledge of the element semantics and cannot always be automatically derived. In this paper, we present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of XQuery Update update primitives. The key feature of the method is the use of an automatic inference method for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as conformance test w.r.t. the type extracted from the target schema S'., Comment: In Proceedings GandALF 2012, arXiv:1210.2028

The wealth of information generated by users interacting with the network and its applications is often under-utilized due to complications in accessing heterogeneous and dynamic data and in retrieving relevant information from sources having possibly unknown formats and structures. Processing complex requests on such information sources is, thus, costly, though not guaranteeing user satisfaction. In such environments, requests are often relaxed and query processing is forced to be adaptive and approximate, either to cope with limited processing resources (QoS-oriented techniques), possibly at the price of sacrificing result quality, or to cope with limited data knowledge and data heterogeneity (QoD-oriented techniques), with the aim of improving the quality of results. While both kinds of approximation techniques have been proposed, most adaptive solutions are QoS-oriented. Additionally, techniques which apply a QoD-oriented approximation in a QoD-oriented adaptive way (called ASAP - Approximate Search with Adaptive Processing - techniques), though demonstrated potentially useful in getting the right compromise between precise and approximate computations, have been largely neglected. In this paper, we first motivate the problem and provide a taxonomy for classifying approximate and adaptive techniques according to the dimensions pointed out above. Then, we show, through some concrete examples, the benefits of using ASAP techniques in two different contexts.

In this paper we propose a matching algorithm for measuring the structural similarity between an XML document and a DTD. The matching algorithm, by comparing the document structure against the one the DTD requires, is able to identify commonalities and differences. Differences can be due to the presence of extra elements with respect to those the DTD requires and to the absence of required elements. The evaluation of commonalities and differences gives raise to a numerical rank of the structural similarity. Moreover, in the paper, some applications of the matching algorithm are discussed. Specifically, the matching algorithm is exploited for the classification of XML documents against a set of DTDs, the evolution of the DTD structure, the evaluation of structural queries, the selective dissemination of XML documents, and the protection of XML document contents.

We extend the standard for object-oriented databases, ODMG, with reactive features, by proposing a language for specifying triggers and defining its semantics. This extension has several implications, thus we make three different specific contributions. First, the definition of a declarative data manipulation language for ODMG, which is missing in the current version of the standard; such a definition requires revisiting data manipulation in ODMG and also addressing issues related to set-oriented versus instance-oriented computation. Then, the definition of a trigger language for ODMG, unifying also the SQL:1999 proposal and providing support for trigger inheritance and overriding. Finally, the development of a formal semantics for the proposed data manipulation and trigger languages.

In this paper we propose a set-oriented rule-based method definition language for object-oriented databases. Most existing object-oriented database systems exploit a general-purpose imperative object-oriented programming language as the method definition language. Because methods are written in a general-purpose imperative language, it is difficult to analyze their properties and to optimize them. Optimization is important when dealing with a large amount of objects as in databases. We therefore believe that the use of an ad hoc, set-oriented language can offer some advantages, at least at the specification level. In particular, such a language can offer an appropriate framework to reason about method properties. In this paper, besides defining a set-oriented rule-based language for method definition, we formally define its semantics, addressing the problems of inconsistency and non-determinism in set-oriented updates. Moreover, we characterize some relevant properties of methods, such as conflicts among method specifications in sibling classes and behavioral refinement in subclasses. Copyright © 2003 John Wiley & Sons, Ltd.

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

We propose an intelligent sensor system based on a new sensing methodology, relying also on 3D map reconstruction techniques, for computing with high precision, in real-time and without human intervention the parameters needed for stream-flow computa- tion: water levels, morphology of the streams of all potentially flooded areas by each controlled stream. The collected data will be continuously transmitted, through a communication infrastructure, to software agents designed to compute the stream-flow and to quantify the spatial distribution of flood risk for each controlled watershed. The computed risks, together with other data coming from other sources (barometric sensors, camera operators of public organizations, emergency agencies, private citizens), will be analyzed by a diagnostic decision system implementing a risk-alert scheduling strategy. This system will be able to diagnose the health state of the controlled environment and to define specialized alarm levels for each potentially interested area that will be used to alert all citizens (ubiquity) locally present (alerting locality).

An active database is a database in which some operations are automatically executed when specified events happen and particular conditions are met. Several systems supporting active rules in an object oriented data model have been proposed. However, several issues related to the integration of triggers with object oriented modeling concepts have not been satisfactorily addressed. We discuss issues related to trigger inheritance and refinement in the context of the Chimera active object oriented data model. In particular, we introduce a semantics for an active object language that takes into account trigger inheritance and supports trigger overriding. Moreover, we state conditions on trigger overriding ensuring that trigger semantics is preserved in subclasses.

A considerable research effort has been devoted in past years to query languages for temporal data in the context of both the relational and the object oriented model. Object oriented databases provide a navigational approach for data access based on object references. We investigate the navigational approach to querying object oriented databases. We formally define the notion of temporal path expression, and we address on a formal basis issues related to the correctness of such expressions. In particular, we focus on static analysis and give a set of conditions ensuring that an expression always results in a correct access at runtime.

In this paper we formalize the object-oriented data model of the Chimera language. This language supports all the common features of object-oriented data models such as object identity, complex objects and user-defined operations, classes, inheritance. Constraints may be defined by means of deductive rules, used also to specify derived attributes. In addition, class attributes, operations, and constraints that collectively apply to classes are supported. The main contribution of our work is to define a complete formal model for an object-oriented data model, and to address on a formal basis several issues deriving from the introduction of rules into an object-oriented data model.

This paper describes the implementation of active rules in the Chimera object-oriented database system. The Chimera active rule language is very rich, since it provides alternative semantics for active rules and combines several innovative features. We show how a run-time support for that active rule language is designed and implemented through the necessary data structures, functional components and algorithms. The paper also compares Chimera active rule implementation with other active database system implementations.

The standard XML query languages, XPath and XQuery, are built on the assumption of a regular structure with well-defined parent/child relationships between nodes and exact conditions on nodes. Full text extensions to both languages allow Information Retrieval (IR) style queries over text-rich documents. Important applications exist for which the purely textual information is not predominant and documents exhibit a structure, that is however not relatively regular. Thus, approaches to relax both content and structure conditions in queries on XML document collections and to rank results according to some measure to assess similarity have been proposed, as well as processing approaches to efficiently evaluate them. In the chapter, the various dimensions of query relaxation and alternative approaches to approximate processing will be discussed.

Ontologies, like any other model, change over time due to modifications in the modeled domain, deeper understanding of the domain by the modeler, error corrections, simple refactoring or shift of modeling granularity level. Local changes usually impact the remainder of the ontology as well as any other data and metadata defined over it. The massive size of ontologies and their possible fast update rate requires automatic adaptation methods for relieving ontology engineers from a manual intervention, in order to allow them to focus mainly on high-level inspection. This paper, in spirit of the Principle of minimal change, proposes a fully automatic ontology adaptation approach that reacts to ontology updates and computes sound reformulations of ontological axioms triggered by the presence of certain preconditions. The rule-based adaptation algorithm covers up to \(\mathcal{SROIQ}\) DL.

The traditional query processing approach, by which queries are executed exactly according to a query execution plan selected before query execution starts, breaks down in heterogeneous and dynamic processing environments that are becoming more and more common as query processing contexts. In such environments, queries are often relaxed and query processing is forced to be adaptive and approximate, either to cope with limited processing resources or with limited data knowledge and data heterogeneity.When approximation and adaptivity are applied in order to cope with limited processing resources, possibly sacrificing result quality, we refer to as Quality of Service (QoS)-oriented techniques. On the other hand, when they are a means to improve the quality of results, in presence of limited data knowledge and data heterogeneity, we refer to as Quality of Data (QoD)-oriented techniques. While both kinds of approximation techniques have been proposed, most adaptive solutions are QoS-oriented. In this chapter, we first survey both kinds of approximation and introduce adaptive query processing techniques; then, we show that techniques which apply a QoD-oriented approximation in a QoD-oriented adaptive way, though demonstrated potentially useful on some examples, are still largely neglected.

The ability of efficiently detecting changes in XML documents is crucial in many application contexts. If such changes are represented as XQuery Update Pending Update Lists (PULs), they can then be applied on documents using XQuery Update engines, and document management can take advantage of existing composition, inversion, reconciliation approaches developed in the update processing context. The paper presents an XML edit-script generator with the unique characteristic of using PULs as edit-script language and improving the state of the art from both the performance and the generated edit-script quality perspectives.

The EDBT'04 Workshop on Database Technologies for Handling XML Information on the Web (DataX'04) was held in Heraklion, Crete, on Sunday 14 March, 2004, and attracted approximately 30 participants from different countries.

In a data stream management system, users may not be acquainted with the actual data arriving on the stream. Therefore, they may issue queries that return an empty result over several windows. In the relational context, relaxation skyline queries have been proposed as a solution to the so-called empty answer problem. Given a query composed of selection and join operations, a relaxation skyline query relies on the usage of a relaxation function (usually, a numeric function) to quantify the distance of each tuple (or pair of tuples in case of join) from the specified conditions and uses a skyline-based semantics to compute the answer. This paper addresses skyline-based relaxation over data streams. Relaxation skyline queries for selection and window-based join over data streams are defined and two different processing algorithms are proposed and experimentally compared.

Data on the Web mostly are in XML format and the need often arises to update their structure, commonly described by an XML Schema. When a schema is modified the effects of the modification on documents need to be faced. XSUpdate is a language that allows to easily identify parts of an XML Schema, apply a modification primitive on them and finally define an adaptation for associated documents, while Eχup is the corresponding engine for processing schema modification and document adaptation statements. Purpose of this demonstration is to provide an overview of the facilities of the XSUpdate language and of the Eχup system.

Automating the management of data throughout its lifecycle requires solutions to effectively and efficiently handle updates, both on data and on the corresponding schema. Such solutions are expecially needed for XML documents and schemata, due to the high dynamicity of the contexts in which they are employed. XML documents need to be kept up-to-date, schemata as well are often subjected to updates and progressively refined to adapt to changing requirements. In this paper, we address the problem of reducing a sequence of updates on a tree representing either an XML document or an XML schema. Reducing means deriving a shorter sequence with the same effect on the tree of the original one. Specifically, we propose a set of reduction rules and an efficient reduction algorithm.

In many emerging XML application contexts and distributed execution environments (like disconnected and cloud computing, collaborative editing and document versioning) the server that determines the updates to be performed on a document, by evaluating an XQuery Update expression, is not always the same that actually makes such updates -represented as Pending Update Lists (PULs)-effective. The process of generating the PUL is thus decoupled from that of making its effect persistent on the document. The PUL executor needs to manage several PULs, that, depending on the application context, are to be executed as sequential or parallel update requests, possibly relying on application-specific policies. This requires some capabilities of dynamic reasoning on updates. In the paper, we state the most relevant properties to reason on, develop the corresponding algorithms and present a PUL handling system, providing an experimental evaluation of this system.

The first international workshop on Updates in XML [1] was held in conjunction with the EDBT/ICDT conference in Lausanne (Switzerland) on March 22, 2010, and attracted approximately 25 participants, culminating with about 40 attending the last session. This paper summarizes the main ideas presented in the workshop as well as interesting perspectives identified by the participants.

Applications that involve data integration among multiple sources often require a preliminary step of data reconciliation in order to ensure that tuples match correctly across the sources. In dynamic settings such as data mashups, however, traditional offline data reconciliation techniques that require prior availability of the data may not be applicable. The alternative, performing similarity joins at query time, is computationally expensive, while ignoring the mismatch problem altogether leads to an incomplete integration. In this paper we make the assumption that, in some dynamic integration scenarios, users may agree to trade the completeness of a join result in return for a faster computation. We explore the consequences of this assumption by proposing a novel, hybrid join algorithm that involves a combination of exact and approximate join operators, managed using adaptive query processing techniques. The algorithm is optimistic: it can switch between physical join operators multiple times throughout query processing, but it only resorts to approximate join operators when there is statistical evidence that result completeness is compromised. Our experiments show that sensible savings in join execution time can be achieved in practice, at the expense of a modest reduction in result completeness.

In applications involving spatio-temporal modelling, granularities of data may have to adapt according to the evolving semantics and significance of data. In this paper we define ST 2_ODMGe, a multigranular spatio-temporal model supporting evolutions , which encompass the dynamic adaptation of attribute granularities, and the deletion of attribute values. Evolutions are specified as Event - Condition - Action rules and are executed at run-time. The event, the condition, and the action may refer to a period of time and a geographical area. The evolution may also be constrained by the attribute values. The ability of dynamically evolving the object attributes results in a more flexible management of multigranular spatio-temporal data but it requires revisiting the notion of object consistency with respect to class definitions and access to multigranular object values. Both issues are formally investigated in the paper.

Measuring the structural similarity between an XML document and a DTD has many relevant applications that range from document classification and approximate structural queries on XML documents to selective dissemination of XML documents and document protection. The problem is harder than measuring structural similarity among documents, because a DTD can be considered as a generator of documents. Thus, the problem is to evaluate the similarity between a document and a set of documents. An effective structural similarity measure should face different requirements that range from considering the presence and absence of required elements, as well as the structure and level of the missing and extra elements to vocabulary discrepancies due to the use of synonymous or syntactically similar tags. In the paper, starting from these requirements, we provide a definition of the measure and present an algorithm for matching a document against a DTD to obtain their structural similarity. Finally, experimental results to assess the effectiveness of the approach are presented.

In this paper we present X-Evolution, a Web system developed on top of a commercial DBMS that allows the specification of schema modifications both on a graphical representation of an XML Schema and through a specifically tailored declarative language. X-Evolution supports facilities for performing schema revalidation only when strictly needed and only on the minimal parts of documents affected by the modifications. Moreover, it supports the automatic and query-based adaptation of original schema instances to the evolved schema.

Due to the heterogeneous nature of XML data for internet applications exact matching of queries is often inadequate. The need arises to quickly identify subtrees of XML documents in a collection that are similar to a given pattern. Similarity involves both tags, that are not required to coincide, and structure, in which not all the relationships among nodes in the tree structure are strictly preserved. In this paper we present an efficient approach to the identification of similar subtrees, relying on ad-hoc indexing structures. The approach allows to quickly detect, in a heterogeneous document collection, the minimal portions that exhibit some similarity with the pattern. These candidate portions are then ranked according to their actual similarity. The approach supports different notions of similarity, thus it can be customized to different application domains. In the paper, three different similarity measures are proposed and compared. The approach is experimentally validated and the experimental results are extensively discussed. © 2007 Elsevier B.V. All rights reserved.

This work-in-progress paper describes the features of the ArHeX similarity-oriented XML processing toolkit. ArHeX is designed to assist in the engineering of XML similarity-oriented applications, supporting the design and evaluation of suitable similarity measures and their associated indexes for each specific application.

XML Schemas describe the structure of valid documents and can be exploited for improving both the efficiency and effectiveness of queries on valid documents. XML Schemas, however, may need to be updated to adhere to new requirements and to face changes in the application domain. Starting from a set of schema modification primitives, in this paper we devise an incremental validation approach that allows to efficiently validate documents, known to be valid for the original schema, for an updated schema. Then, we enhance the approach to adapt the documents to the new schema. Experiments prove that our approach increases the performance of standard validation algorithms in this setting and that the cost of the adaptation process is limited.

The large amount and heterogeneity of XML documents on the Web require the development of clustering techniques to group together similar documents. Documents can be grouped together according to their content, their structure, and links inside and among documents. For instance, grouping together documents with similar structures has interesting applications in the context of information extraction, of heterogeneous data integration, of personalized content delivery, of access control definition, of web site structural analysis, of comparison of RNA secondary structures. Many approaches have been proposed for evaluating the structural and content similarity between tree-based and vector-based representations of XML documents. Link-based similarity approaches developed for Web data clustering have been adapted for XML documents. This chapter discusses and compares the most relevant similarity measures and their employment for XML document clustering.

The structure of XML documents, expressed as XML schemas [6], can evolve as well as their content. Systems must be frequently adapted to real-world changes or updated to fix design errors and thus data structures must change accordingly in order to address the new requirements. A consequence of schema evolution is that documents instance of the original schema might not be valid anymore. Currently, users have to explicitly revalidate the documents and identify the parts to be updated. Moreover, once the parts that are not valid anymore have been identified, they have to be explicitly updated. All these activities are time consuming and error prone and automatic facilities are required.

Handling the heterogeneity of structure and/or content of XML documents for the retrieval of information is a fertile field of research nowadays. Many efforts are currently devoted to identifying approximate answers to queries that require relaxation on conditions both on the structure and the content of XML documents [1,2,4,5]. Results are ranked relying on score functions that measure their quality and relevance and only the top-k returned.

Due to the heterogeneous nature of XML data for internet applications exact matching of queries is often inadequate. The need arises to quickly identify subtrees of XML documents in a collection that are similar to a given pattern. In this paper we discuss different similarity measures between a pattern and subtrees of documents in the collection. An efficient algorithm for the identification of document subtrees, approximately conforming to the pattern, by indexing structures is then introduced.

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both.

In this paper we discuss access to object attributes in a multigranular temporal object-oriented data model that handles the expiration of dynamic attributes, according to the age of data and their granularity [4]. Different strategies can be applied, with respect to available data and to the preferences specified by the user on either accuracy or efficiency in executing a query. We devise some properties of object access that can be applied to speed up the query process, such as the invariance of the queries results with respect to expiration operations, and the static detection of unsolvability of a query.

A large percentage of data managed by a variety of different application domains has spatiotemporal characteristics. Unfortunately, traditional geographical information systems do not allow for an easy representation of temporal aspects of spatial data. Moreover, they do not usually support the representation of data at multiple levels of granularity. In this paper we present a multigranular spatiotemporal data model. Our model extends the ODMG model with multiple spatial and temporal granularities. In particular, the model allows for an uniform management of two kinds of spatiotemporal objects: moving entities (e.g. cars, planes, etc.) and temporal maps (i.e., maps representing the change over time of a given geographic area). It also provides a framework for mapping the movement of an entity such as a car onto an underlying geographic area. The model we propose relies on a standard definition of temporal granularity. On the other hand, the representation of spatial entities at multiple granularities is obtained by applying model oriented map generalization principles. In particular, we consider a set of generalization operators that guarantee topological consistency.

In this paper we investigate some issues arising from the introduction of multiple temporal granularities in an object-oriented data model. Although issues concerning temporal granularities have been investigated in the context of temporal relational database systems, no comparable amount of work has been done in the context of object-oriented models. Moreover, the main drawback of the existing proposals is the lack of a formal basis--which we believe is essential to manage the inherent complexity of the object-oriented data model. In this paper, we define a comprehensive temporal object-oriented data model supporting multiple temporal granularities. We formally define the main notions of the data model such as types, legal values, classes, and objects. We address issues related to inheritance, type refinement, and substitutability. Finally, we describe the implementation of the presented model on top of an ODMG compliant DBMS.

In this paper we address the problem of evolving a set of DTDs so to obtain a description as precise as possible of the structures of the documents actually stored in a source of XML documents. This problem is highly relevant in such a dynamic and heterogeneous environment as the Web. The approach we propose relies on the use of a classification mechanism based on document structure and on the use of data mining association rules to find out frequent structural patterns in data.

The main key feature of temporal databases is to maintain all values taken by object attributes over time. Since historical information may be needed at different levels of detail, multigranular temporal databases have been introduced, in which different attributes can be stored at different temporal granularities. An important issue that has not been addressed, however is that the required level of detail does not only depend on attribute semantics, rather it is often related to how recent the data are. It is quite natural that recent data are needed at greater level of detail, whereas less detail is needed as data age. As an extreme case, data can also expire, that is, they are no longer needed after a certain period of time. In this paper we address the problem of evolution and expiration of historical data in a multigranular temporal object model.

Exceptions in database systems can be used for two different purposes: to store data not conforming to the description provided by the database schema, that is, exceptional data; and to handle exceptional situations during processing, that is, the usual execution exceptions of programming languages. In this paper we survey approaches to both kinds of exceptions in OODBMSs, we discuss some uses of exceptions peculiar to databases, and relate exceptions with triggers, a typical database functionality.