Underwater noise pollution from shipping activities is widely recognised as a significant threat to marine life. Noise emitted by vessels can have various detrimental effects on fish and marine ecosystems. Accurately estimating and analysing vessel-generated underwater noise is therefore of critical importance for the protection and conservation of marine environments. In this paper, we present an enhanced version of our model for the spatiotemporal characterisation of vessel-generated underwater noise, with a focus on improving its scalability. The original model was limited to fishing vessels and relied on Automatic Identification System (AIS) data to reconstruct trajectories, as well as engine horsepower to estimate emitted noise. Here, we generalise the approach to include all vessel categories — including tankers, cruise ships, and recreational boats — still relying on AIS data, but estimating noise as a function of vessel length overall (LOA) and category, since horsepower information is not available for all vessels in the dataset. We broaden the study area to include the Central Adriatic Sea, in addition to the Northern part previously considered. The enlarged area and the substantially greater volume of AIS data introduce significant computational challenges, making scalability a primary concern. We address these challenges through a comprehensive analysis of optimisation strategies to improve query execution performance. In particular, we restructure the computational pipeline by implementing table partitioning and leveraging parallelisation techniques. Specifically, we employ PostgreSQL’s native parallel query execution and implement multiple partitioning strategies, including range, hash, and list partitioning. We further explore spatial partitioning through space tiling, comparing regular, adaptive, and k-d tree-based grids. Finally, we leverage the Citus extension to distribute computation across four and eight nodes. Our approach improves computational efficiency while preserving the accuracy of noise calculation, offering a scalable solution for large datasets.

Distributed MobilityDB [2] (DistMobilityDB) extends MobilityDB [7], the PostgreSQL/PostGIS-based moving-object database, into a clustered, SQL-native system for large spatiotemporal trajectory datasets. It introduces a distributed architecture that (1) partitions data with spatiotemporal awareness, (2) plans and executes a wide range of distributed spatiotemporal queries (range, distance/knn, intersection, self-joins) across worker nodes, and (3) is delivered as a PostgreSQL extension so it can be deployed on existing servers without recompilation or downtime. Experiments on real (AIS ship) and synthetic (BerlinMOD) datasets across on-prem and cloud clusters show that DistMobilityDB achieves scalable performance while preserving MobilityDB's rich SQL semantics.

The propagation of Internet-of-Things (IoT) sensors aboard moving objects has led to continuous spatiotemporal data streams that demand on-device, low-latency analysis. However, the underlying systems for processing this streaming data are ill-prepared. On the one hand, common stream processing engines lack support for spatiotemporal operations. On the other hand, existing libraries for spatiotemporal data are optimized for historical data rather than real-time processing. To bridge this gap, we present MobilityNebula, an integration between MEOS (Mobility Engine Open Source), a lightweight C library for spatiotemporal data management, and NebulaStream, a stream processing system designed for the edge-fog-cloud continuum. We evaluated MobilityNebula by deploying the system on edge devices, ingesting data from the Belgian railway operator (SNCB) trains, and performing real-time geospatial processing for point-based queries.

Today it is simple to collect and transmit GNSS data from vehicles with a high frequency. However, there is a storage and processing cost related to handling the data. Further, some data has limited value, e.g., redundant GNSS data from a vehicle stopped at an intersection. In this paper, sampling methods for GNSS data focusing on time, distance, speed, and heading changes are systematically analyzed. The goal is to retain only valuable data. A set of metrics is proposed to quantify the value of the data, e.g., no redundancy and retention of the spatial and temporal distributions. An existing commercial approach to GNSS-based travel time computation in road networks is used to measure if the sampled GNSS is accurate for this important purpose. The results show that sampling methods using individual properties, such as time, space, or speed, have their own strengths and weaknesses. However, with hybrid methods, it is possible to retain the strengths and eliminate most weaknesses. Using a large, real-world GNSS dataset, we show that a hybrid method that retains only 20 % of the original data can achieve travel time estimation with an error of just 1.0 – 1.3%.

The ship trajectories collected by the Automatic Identification System (AIS) are widely used in maritime applications. However, a significant issue with AIS data is that large AIS gaps occur. Existing trajectory imputation methods for AIS data have three main limitations: (1) the temporal aspect is ignored; (2) the methods fall short when dealing with complex ship movements; (3) the common-route assumption does not always hold. To overcome these limitations, we propose TrajImpMC, a tracking-based framework that uses polygon-based ship location estimates from multiple cameras to impute large AIS gaps. TrajImpMC combines speed constraints and Kalman filters, and can return imputed trajectories that contain both spatial and temporal information. Extensive experiments are conducted on real datasets. In terms of the quality of the imputed trajectories, TrajImpMC improves the RMSE errors by at least one order of magnitude over two existing state-of-the-art AIS imputation methods. In addition, a visual comparison shows that the imputed trajectories of TrajImpMC align very well with the real ship trajectories during AIS gaps. The code for this paper is available at: https://github.com/songwu0001/TrajImpMC.

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatiotemporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels’ trajectories from Automatic Identification System (AIS) data and on their deployment in a spatiotemporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels’ engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories’ information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic Sea, an area of the Mediterranean Sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatiotemporal operators and indices improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.

Monitoring roundabouts and signalized intersections in a road network is important, e.g., to reduce travel time and greenhouse gas emissions. The monitoring of such junctions is a challenging problem, and current approaches mainly use high-cost solutions for a selected few. In this work, we present a framework for the automated identification and monitoring of all junctions in a road network. The framework utilizes detailed trajectory data or high-level segment-based data to compute travel time and energy consumption for all turn directions. These metrics are then aggregated per junction to enable a fair comparison between roundabouts and intersections. The aggregated metric is used to provide an overview of all junctions and to pinpoint those performing poorly. An analysis of 1,394 junctions using 334,081 trajectories quantifies the different benefits of roundabouts and intersections, e.g., the travel time in roundabouts varies little, and turns are 21% to 155% more energy-consuming than going straight in intersections. Further, the aggregated junction metric makes it simple to monitor all analyzed junctions and detect the worst-performing. The analysis also clearly shows the benefits of trajectory data over segment-based data for junction monitoring.

The idea of generalized indices is one of the success stories of database systems research. It has found its way to implementation in common database systems. GiST (Generalized Search Tree) and SP-GiST (Space-Partitioned Generalized Search Tree) are two widely-used generalized indices that are typically used for multidimensional data, e.g., to index spatial or spatio-temporal data. Currently, the generalized indices GiST and SP-GiST represent one database object using one index entry, e.g., a scalar value or abounding box for each spatial or spatio-temporal object. However, when dealing with complex objects, e.g., moving object trajectories, a single entry per object is inadequate for creating efficient indices. Previous research has highlighted that splitting trajectories into multiple sub-trajectories or bounding boxes prior to indexing can enhance query performance as it leads to a higher index filter. In this paper, we introduce MGiST and MSP-GiST, the multi-entry generalized search tree counterparts of GiST and SP-GiST, respectively, that are designed to enable the partitioning of objects into multiple entries during insertion. The methods for decomposing a complex object into multiple sub-objects differ from one data type to another, and may depend on some domain-specific parameters. Thus, MGiST and MSP-GiST are designed to allow for pluggable modules that aid in optimizing the split of an object into multiple sub-objects. We demonstrate the usefulness of MGiST and MSP-GiST using a trajectory indexing scenario, where we realize several trajectory indexes using MGiST and MSP-GiST and instantiate these search trees with trajectory-specific splitting algorithms. We create and test the performance of several multi-entry versions of widely-used spatial index structures, e.g., R-Tree, Quad-Tree, and KD-Tree. We conduct evaluations using both synthetic and real-world data, and observe up to an order of magnitude enhancement in performance of point, range, and nearest neighbor queries.

Air pollution monitoring requires a large number of expensive devices, especially in large cities. To reduce the cost of this process, the use of mobile devices has been proposed. Some proposals promote the use of cheap sensors on board of public buses instead of just a limited number of specialized vehicles. Analyzing the data provided by these mobile devices is computationally costly and complex with traditional database tools. Instead, we propose using MobilityDB, a novel database implemented as an extension to PostgreSQL and PostGIS, which provides support for storing and querying geospatial trajectory data and their time-varying properties (like air-pollution parameters), implementing persistent database types and query operations for such data. We use public data from the city of Delhi to show the viability and advantages of our approach and how analytical queries are expressed in a concise and elegant way using MobilityDB data types and functions.

Recent advances, especially in deep learning, allow to effectively detect ship targets in surveillance videos. However, the translation of these detections to the real-world locations of ships has not been sufficiently explored. The common approach in the literature is using a transformation matrix to convert a pixel to a real-world coordinate. However, this approach has three shortcomings: first, a set of reference point pairs has to be manually prepared to establish the matrix; second, the matrix always maps a pixel to the same real-world coordinate, ignoring that there is no one-to-one correspondence between discrete pixel coordinates and continuous real-world coordinates; third, this approach can only work with one camera. In light of this, we propose a technique PixelToRegion that explicitly takes into account the uncertainty in coordinate conversion by mapping each pixel to a spatial polygon. Next, we propose a new algorithm MCbSLE that can estimate ship locations using pixel sets from multiple cameras. The precision of location estimation by MCbSLE is enhanced through spatial intersection between polygons from different cameras. Experiments are conducted under 16 carefully designed multi-camera settings to evaluate MCbSLE w.r.t. four factors: different ports, the number of cameras, the distance between cameras, and camera headings. Results on one-day ship trajectory data show that (1) an 79.8% accuracy in the number of coordinates can be achieved by MCbSLE when there are no more than 10 ships in camera views; (2) using multiple cameras can improve the precision of location estimation by one order of magnitude compared with using one camera.

In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

Accurate estimation of shipping CO2 emissions is important for developing regulations to combat the greenhouse effect. Many shipping CO2 emissions models have been proposed in the past decades. However, most of them are only validated for a few specific ships, and there is a lack of data-driven validation and comparison of these models on a large scale. To fill this gap, this study proposes a general evaluation framework to quantitatively validate and compare different emission models. This framework is based on data integration of three types of data sources: ship technical details, AIS trajectory, and weather. Along with emission models, these data are fed into three carefully-designed modules that perform analysis at both grid and trajectory level as well as use annually aggregated fuel consumption ground truth. Extensive experiments are conducted on one-month data from 1,571 ships passing Danish waters to demonstrate the utility of the framework and insights into the accuracy of five popular CO2 emission models are presented.

Abstract Document stores have gained popularity among NoSQL systems mainly due to the semi-structured data storage structure and the enhanced query capabilities. The database design in document stores expands beyond the first normal form by encouraging de-normalization through nesting. This hinders the process, as the number of alternatives grows exponentially with multiple choices in nesting (including different levels) and referencing (including the direction of the reference). Due to this complexity, document store data design is mostly carried out in trial-and-error or ad-hoc rule-based approaches. However, the choices affect multiple, often conflicting, aspects such as query performance, storage space, and complexity of the documents. To overcome these issues, in this paper, we apply multicriteria optimization. Our approach is driven by a query workload and a set of optimization objectives. First, we formalize a canonical model to represent alternative designs and introduce an algebra of transformations that can systematically modify a design. Then, using these transformations, we implement a local search algorithm driven by a loss function that can propose near-optimal designs with high probability. Finally, we compare our prototype against an existing document store data design solution purely driven by query cost, where our proposed designs have better performance and are more compact with less redundancy.

Detection of fishing activities in trajectory data is important for authorities to develop fishery management policies and combat illegal, unreported, and unregulated (IUU) fishing at sea. However, the complex movement patterns of fishing activities challenge existing trajectory segmentation approaches, which may not identify complete fishing activities. In light of this, we propose a window-based trajectory segmentation algorithm which aims to detect fishing activities as completely as possible. Firstly, we introduce a visualization-based technique TPoSTE to help design features characterizing different movement patterns. Secondly, a window-based segmentation algorithm WBS-RLE is proposed to split a trajectory into fishing and non-fishing segments. WBS-RLE first utilizes a pre-trained classifier to label windows in a trajectory as fishing or non-fishing, then it uses the run-length encoding technique to merge those labeled windows into complete fishing activities. The effectiveness of our approach and its advantages over existing approaches are evaluated on a real-world trajectory dataset.

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a time-efficient spatial blocking technique (QuadFlex), compares pairwise the spatial entities in the same block, ranks the pairs using Pareto optimality with theSkyRankalgorithm, and finally, classifies the pairs with our novelSkyEx-*family of algorithms that yield 0.85precisionand 0.85recallfor a manually labeled dataset of 1,500 pairs and 0.87precisionand 0.6recallfor a semi-manually labeled dataset of 777,452 pairs. Moreover, we provide a theoretical guarantee and formalize theSkyEx-FESalgorithm that explores only 27 percent of the skylines without any loss inF-measure. Furthermore, our fully unsupervised algorithmSkyEx-Dapproximates the optimal result with anF-measureloss of just 0.01. Finally,QuadSkyprovides the best trade-off betweenprecisionandrecall, and the bestF-measurecompared to the existing baselines and clustering techniques, and approximates the results of supervised learning solutions.

Various applications process geospatial trajectories of moving objects, such as cars, ships and robots. There is thus a need for a common conceptual framework to model and manage these objects, as well as to enable data interoperability across tools. The International Organization for Standardization ISO® has responded to this need and created the standard ISO 19141- Schema for moving features. Among its types, it defines a schema for rigid temporal geometries, which represent the movement of spatial objects translating and rotating over time, while preserving a fixed shape. Despite the abundance of these objects in real-world, there exists no reference implementation of this type of data in a common system, which causes them to usually be represented as temporal points without taking into account their spatial extents and shapes. In this paper, we aim to provide an implementation of rigid temporal geometries into MobilityDB, an open-source moving object database, that extends PostgreSQL and PostGIS. We provide a data model for rigid temporal geometries and propose efficient algorithms for the operations defined in ISO 19141. A use case on real AIS ship trajectories is illustrated to validate the proposed implementation. A synthetic data generator for temporal geometries is also proposed. Finally, we review the standard from an implementation point of view and provide insights on possible improvements.

A single type of data store can hardly fulfill every end-user requirements in the NoSQL world. Therefore, polyglot systems use different types of NoSQL datastores in combination. However, the heterogeneity of the data storage models makes managing the metadata a complex task in such systems, with only a handful of research carried out to address this. In this paper, we propose a hypergraph-based approach for representing the catalog of metadata in a polyglot system. Taking an existing common programming interface to NoSQL systems, we extend and formalize it as hypergraphs. Then, we define design constraints and query transformation rules for three representative data store types. Next, we propose a simple query rewriting algorithm from the metadata of the catalog to underlying data store specific ones and provide a prototype implementation. Furthermore, we introduce a storage statistics estimator on the underlying data stores. Finally, we show the feasibility of our approach on a use case of an existing polyglot system, and its usefulness in metadata and physical query path calculations.

Nowadays, the collection of moving object data is significantly increasing due to the ubiquity of GPS-enabled devices. Managing and analyzing this kind of data is crucial in many application domains, including social mobility, pandemics, and transportation. In previous work, we have proposed the MobilityDB moving object database system. It is a production-ready system, that is built on top of PostgreSQL and PostGIS. It accepts SQL queries and offers most of the common spatiotemporal types and operations. In this paper, to address the scalability requirement of big data, we provide an architecture and an implementation of a distributed moving object database system based on MobilityDB. More specifically, we define: (1) an architecture for deploying a distributed MobilityDB database on a cluster using readily available tools, (2) two alternative trajectory data partitioning and index partitioning methods, and (3) a query optimizer that is capable of distributing spatiotemporal SQL queries over multiple MobilityDB instances. The overall outcome is that the cluster is managed in SQL at the run-time and that the user queries are transparently distributed and executed. This is validated with experiments using a real dataset, which also compares MobilityDB with other relevant systems.

Mobility applications involve large amounts of data that must be managed and queried in a scalable way. MobilityDB1 is an SQL moving object database system. It is an extension of PostgreSQL and PostGIS that supports storing and querying mobility data. This paper demonstrates the distributed query management capabilities in MobilityDB using a cluster that contains 2 billion real AIS ship trajectory points obtained from the Danish Maritime Authority.

Document stores have become one of the most popular NoSQL systems, mainly due to their semi-structured data storage structure and well-developed query capabilities. The semi-structured nature allows them to have database designs beyond traditional normalization theories. This makes the database design decisions more complicated with a myriad of possibilities. Thus, the database design process for them has resorted to ad-hoc trial and error methods. However, having a good database design is essential for any data storage system’s performance, and bad design decisions cannot always be compensated by adding more powerful hardware. Thus, in this work, we propose DocDesign, a decision aid tool for document store database design. DocDesign allows its users to evaluate different database designs for data storage requirements under a particular workload. Through DocDesign, users can make informed decisions for a design by evaluating the estimated storage statistics and query runtimes without testing it on an actual document store. DocDesign also generates design specific queries for the input workload. This not only cuts down the time and the effort taken in design decision making and development but also save money spent on fixing poor designs in the long run. On-site, we will showcase how DocDesign facilitates the design decision-making process for MongoDB with both synthetic and real-world examples.

Despite two decades of research in moving object databases and a few research prototypes that have been proposed, there is not yet a mainstream system targeted for industrial use. In this article, we present MobilityDB, a moving object database that extends the type system of PostgreSQL and PostGIS with abstract data types for representing moving object data. The types are fully integrated into the platform to reuse its powerful data management features. Furthermore, MobilityDB builds on existing operations, indexing, aggregation, and optimization framework. This is all made accessible via the SQL query interface.

OpenStreetMap (OSM) is a popular community-driven mapping platform with voluntary contributions from (amateur) cartographers. However, it is a difficult process for the cartographer to identify the areas where she can best contribute to OSM. Furthermore, the current OSM spatial entities are missing many tags; for example, top three road network tags, Name, Source, and Surface, are available only for the 10% of the total road segments. Our paper aims to improve the quantity and quality of the road network tags by actively pushing the nearest road segments for the cartographer to be mapped. We propose a push-based spatial crowdsourcing method to achieve this objective, and validate it by focusing on road segments in OSM. Specifically, we formally define the batch-based maximum road segment task assignment problem and suggest methods based on heuristics like travel distance and road segment task grouping. Finally, our experimental evaluation verify the applicability of our assignment solutions by comparing the resulting number of assigned tasks. With regard to the number of assigned road segments, our junctions-based and road segment-based heuristic methods, outperform the baseline methods by five and two times, respectively.

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. Location-based sources offer rich spatial information describing the semantics of locations. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities of interest, describe them with different attributes, and sometimes provide contradicting information. Hence, the problem of finding which pairs of spatial entities belong to the same physical spatial entity demands specific attention. We propose a solution (QuadSky) to the problem of spatial entity linkage across diverse location-based sources. QuadSky starts with a spatial blocking technique (QuadFlex) that inherits the concept and the complexity from the quadtree algorithm but improves the splitting technique not to separate nearby points. After comparing the spatial entities of the same block, we propose a novel algorithm, referred to as SkyEx that separates the pairs considered as a match (positive class) from the rest (negative class) by using Pareto optimality. SkyEx does not require weights on the attributes, scoring function, or a training set. QuadSky achieves 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, QuadSky provides the best trade-off between precision and recall and consequently, the best F-measure compared to the existing baselines.

This paper demonstrates the MobilityDB moving object database system. It is an extensive implementation on top of PostgreSQL and PostGIS with multiple novel aspects. MobilityDB defines multiple spatiotemporal types for moving geometry and geography points, as well as for temporal integers, reals, Booleans, and strings. It also defines a rich set of operations on these types. The types are supported with spatiotemporal index access methods by extending GiST (Generalized Search Tree) and SP-GiST (Space Partitioning GiST). The query interface is SQL. MobilityDB thus extends the PostgreSQL optimizer with statistics collectors and selectivity estimation functions. It is available as open source. The demonstration includes a scenario with multiple queries, and a publicly accessible query interface on the Web.

ETL processes are the backbone component of a data warehouse, since they supply the data warehouse with the necessary integrated and reconciled data from heterogeneous and distributed data sources. However, the ETL process development, and particularly its design phase, is still perceived as a time-consuming task. This is mainly due to the fact that ETL processes are typically designed by considering a specific technology from the very beginning of the development process. Thus, it is difficult to share and reuse methodologies and best practices among projects implemented with different technologies. To the best of our knowledge, no attempt has been yet dedicated to harmonize the ETL process development by proposing a common and integrated development strategy. To overcome this drawback, in this paper, a framework for model-driven development of ETL processes is introduced. The benefit of our framework is twofold: (i) using vendor-independent models for a unified design of ETL processes, based on the expressive and well-known standard for modeling business processes, the Business Process Modeling Notation (BPMN), and (ii) automatically transforming these models into the required vendor-specific code to execute the ETL process into a concrete platform.

Decisional systems are crucial for enterprise improvement. They allow the consolidation of heterogeneous data from distributed enterprise data stores into strategic indicators. An essential component of this data consolidation is the Extract, Transform, and Load (ETL) process. In the research literature there has been very few work defining conceptual models for ETL processes. At the same time, there are currently many tools that manage such processes. However, each tool uses its own model, which is not necessarily able to communicate with the models of other tools. In this paper, we propose a platform-independent conceptual model of ETL processes based on the Business Process Model Notation (BPMN) standard. We also show how such a conceptual model can be implemented using Business Process Execution Language (BPEL), a standard executable language for specifying interactions with web services.

Data warehouses and On-Line Analytical Processing (OLAP) provide an analysis framework supporting the decision making process. In many application domains, complex analysis tasks often require to take geographical information into account. Several proposals exist for integrating OLAP and Geographic Information Systems (GIS). However, there are very few attempts to support continuous fields, i.e., phenomena that are perceived as having a value at each point in space and/or time. Examples of such phenomena include temperature, altitude, or land use. In this paper, we extend a conceptual multidimensional model with continuous fields, showing that this can be achieved by defining an appropriate data type that encapsulates the different operations needed for manipulating such fields. We also define a query language based on relational calculus that allows expressing spatial OLAP queries involving continuous fields, and use this language to formally characterize this class of queries.

Materialization is a powerful and ubiquitous abstraction pattern for conceptual modeling that relates a class of categories (e.g., models of cars) and a class of more concrete objects (e.g., individual cars). This paper presents materialization as a generic relationship between two classes of objects and describes an abstract implementation of it. The presentation is abstract in that it is not targeted at a specific object system. The target system is supposed to provide: 1) basic object-modeling facilities, supplemented with an explicit metaclass concept and 2) operations for dynamic schema evolution like creation or deletion of a subclass of a given class and modification of the type of an attribute of a class. The presentation is generic in that the semantics of materialization is implemented in a metaclass, which is a template to be instantiated in applications. Application classes are created as instances of the metaclass and they are thereby endowed with structure and behavior consistent with the generic semantics of materialization.

Although many temporal models have been proposed in the literature, there is still need for a conceptual model capturing the essential semantics of time-varying information that is free of implementation concerns. This paper first discusses important criteria to be considered when assessing the “conceptual” quality of a temporal model. Then, it presents the main temporal features of MADS, a spatiotemporal conceptual model. The focus is on identifying issues that are either open or controversial, and discussing alternatives, if any. Finally, it is shown how the model may be implemented on top of either TSQL2 or a traditional, non-temporal data model. 1.1 Keywords Temporal databases, conceptual modeling, data semantics

Intensional answers are conditions that tuples of values must satisfy to belong to the usual extensional answer of a query addressed to a deductive database. The authors review the concept of intensional answers and introduce a general method for generating them as logical consequences of the query and of deduction rules. The authors show how integrity constraints can filter out inadequate answers and produce simpler and more informative answers. An efficient organization for the combination of answers and constraints is described. The introduction of negation in queries and in the body of deduction rules is discussed. Beyond the mechanics of answer generation, the interest of the approach also depends on a strategy for selecting answers to a user submitting a query. This requires techniques for user modeling and dialogue management similar to those required for expert systems.>