The Text-to-SQL task is defined as “given a relational database and a natural language sentence that describes a question on the database, generate an SQL query over the database that expresses the question”. Text-to-SQL strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks, but their performance is significantly less for real-world databases. Some of the reasons for this performance decrease lie in the mismatch between the end user’s view of the data and the organization and naming conventions of the database schema, and in the differences between the end user’s data semantics and the encoding of such semantics in the database. This article then proposes an LLM-based strategy to compile natural language questions into SQL queries that uses a knowledge graph and incorporates a dynamic few-shot examples technique. The implementation of the strategy leverages a database keyword search tool and specific naming conventions to expose the knowledge graph to an LLM thereby simplifying the text-to-SQL task. The article includes experiments with a real-world, proprietary relational database and the Mondial database to assess the performance of the proposed strategy. The experiments suggest that the strategy achieves an accuracy on challenging relational databases that surpasses state-of-the-art approaches on the same databases.

Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data.

The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-time. This enables real-time analyses on mobility data in urban settings, which in turn have the potential to substantially improve traffic conditions, analyze congested areas, detect events in (quasi) real-time, and so on. While many existing approaches characterize past movements of moving objects from historical trajectory data, or address the problem of finding out clusters of moving objects from data streams, such approaches fail to capture how movement behaviors unravel over time - for instance, they fail to capture typically trafficked routes or traffic jams. In this work we propose NET-CUTiS, a novel approach that addresses the problem of discovering and monitor the evolution of clusters of trajectories over road networks from trajectory data streams. We conduct several experiments that demonstrate the validity of our proposal in terms of clustering quality and run-time performance.

This paper1 first argues that ontology design may benefit from treating ontologies as theories and from the definition of a set of operations that map ontologies into ontologies, especially their constraints. The paper then defines the class of ontologies used and proposes four operations to manipulate them. It proceeds to discuss how the operations may help design new ontologies. The core of the paper describes an implementation of the operations as a Protégé plug-in, called OntologyManagerTab, and includes use case examples to validate the discussion.

Time dependent networks are of key importance to allow computing precise travel times taking into consideration moving object's departure time. However the computation of time functions that are used to annotate time dependent networks are challenging since we must cope with noisy and incomplete traffic data. Recent related works adopt approaches that build Piecewise linear functions, which do not cope with aforementioned problems. In this work, we propose a new method for generating Piecewise linear functions by applying a map-matching technique allied to a curve smoothing approach in order to treat outliers and complete data. We performed experiments using real trajectory data and compared our results with a baseline. Preliminary results show that our approach generates time functions with better approximation than the baseline competitor.

Recent research has pointed out the needs and advantages of the semantic enrichment of movement data, a process where trajectories are partitioned into homogeneous segments that are annotated with contextual information. However, the lack of a comprehensive and well-defined framework for the enrichment makes this process difficult and error-prone. In this paper, we therefore propose a conceptual framework for the semantic enrichment of movement data, which benefits from the emerging Web of Data (or Linked Open Data) both as a unifying formalism and as the source of contextual data, which can be greatly useful for trajectories enrichment. Moreover, the semantic structure of such sources makes it easier to share and process enriched trajectories. We illustrate the enrichment process by presenting a case study in the tourism domain.

Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subse-quently, they fail to capture the behavior of moving objects over time. By continuously tracking sub-trajectories of moving object at each time window, it becomes possible to gain insight on the current behavior and potentially detect mobility patterns in real time. In our previous work [1], we proposed CUTiS, an incremental algorithm for discovering and maintaining the density-based clusters in trajectory data streams, while tracking the evolution of the clusters. This paper extends [1] to CUTiS* by proposing an indexing structure for sub-trajectory data based on a space-filling curve. The proposed index improves the performance of our approach without losing quality in the clusters results as we show in our experiments conducted on a real dataset.

The main input data for a traffic management system are trajectories generated by active GPS devices installed in vehicles. In this case, such trajectories can be understood as mobile traffic sensors. A vehicle raw trajectory may be considered as a continuous data stream generated by a GPS device, installed in the vehicle, that continuously transmit its position. In some cities, such as the City of Rio de Janeiro, each bus circulating in the city has to be equipped with such device. Using this fact, the City Hall of Rio de Janeiro offers a public Web service that publishes, at about every minute, the current GPS position of all buses. However, this public data service offers only the instantaneous data, in other words, there is no historical data available. For this reason, it is necessary to develop another service that periodically queries this data and stores its entries for future processing and for useful applications, such as traffic analysis and planning. The objective of this paper is to present the Buses In Rio tool that implements this functionality and provides access to bus trajectory data since June, 2014. The current database has approximately 2 billion GPS samples.

In this paper we propose a new kind of kNN query on time-dependent network, which aims at finding k points of interest that are closest in time to a query point. This query is useful for many kind of applications where a user/customer should ask for a service provided by many moving providers (e.g. Taxi drivers, ambulances, food delivers, etc). We described our solution and present experimental results comparing our proposed algorithm to a baseline approach. The experimental results show that our approach is efficient and effective.

Trajectory pattern mining allows characterizing movement behavior, which leverages new applications and services. Most existing approaches analyse the whole object trajectory rather that the current movement. Besides existing approaches for online pattern discovery are restricted to instantaneous positions. Subsequently, they fail to capture the movement behaviour along time. By continuously tracking moving objects sub-trajectories at each time window, rather than just the last position, it becomes feasible to gain insight on the current behaviour, and potentially detect mobility patterns in real time. This demonstration presents a novel framework for online mobility pattern discovery in sub-trajectory data streams. Key innovations include: (i) Online discovery of mobility patterns and pattern evolution by tracking the sub-trajectories of moving objects, (ii) A novel structure, called micro-group, to represent the relationship among moving objects, and (iii) An incremental algorithm to maintain micro-groups and to capture their evolution on highly dynamic sub-trajectory data. We present various demonstration scenarios using a real data set.

The W3C RDB2RDF Working Group proposed a standard language to map relational data into RDF triples, called R2RML. However, creating R2RML mappings may sometimes be a difficult task because it involves the creation of views (within the mappings or not) and referring to them in the R2RML mapping. To overcome such difficulty, this paper first proposes algebraic correspondence assertions, which simplify the definition of relational-to-RDF mappings and yet are expressive enough to cover a wide range of mappings. Algebraic correspondence assertions include data-metadata mappings (where data elements in one schema serve as metadata components in the other), mappings containing custom value functions (e.g., data format transformation functions) and union, intersection and difference between tables. Then, the paper shows how to automatically compile algebraic correspondence assertions into R2RML mappings.

Positioning systems, combined with inexpensive communication technologies, open interesting possibilities to implement real-time applications that monitor vehicles and support decision making. This paper first discusses basic requirements for proactive real-time monitoring applications. Then, it describes how to structure and geo-reference unstructured text information available on the Internet, with a focus on road conditions change and using available geocoding services. Lastly, the paper outlines an application that monitors a fleet of trucks and incorporates proactive features.

Data mashups constructed from independent sources may contain inconsistencies, puzzling the user that observes the data. This paper formalizes the notion of consistent data mashups and introduces a heuristic procedure to compute such mashups.

We analyze urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving within a city. To accomplish this, we propose a methodology based on complex network analysis: we build a network of points of interests by connecting places by the individual trajectories passing through them. From such network we compute communities finding groups places highly connected by the mobility of the individuals. We present a case study on real trajectory dataset on the city of Milan, showing a complementary view on the urban mobility that is not covered by the state-of-the art techniques on mobility analysis.

A massive amount of data on moving object trajectories is available today. However, it is still a major challenge to process such information in order to explain moving object interactions, which could help in revealing non-trivial behavioral patterns. To that end, we consider a complex networks-based representation of trajectory data. Frequent encounters among moving objects (trajectory encounters) are used to create the network edges whereas nodes represent trajectories. A real trajectory dataset of vehicles moving within the City of Milan allows us to study the structure of vehicle interactions and validate our method. We create seven networks and compute the clustering coefficient, and the average shortest path length comparing them with those of the Erdős-Rényi model. Our analysis shows that all computed trajectory networks have the small world effect and the scale-free feature similar to the internet and biological networks. Finally, we discuss how these results could be interpreted in the light of the traffic application domain.

In this paper, we present a three-level ontology-based framework for effectively designing GAV data integration systems. In our approach, the mediated schema is represented by a domain ontology, which provides a conceptual representation of the application. Each local source is described by an application ontology, whose vocabulary is restricted to be a subset of the vocabulary of domain ontology. The three-level architecture permits dividing the mapping definition in two stages: local mappings and mediated mappings. Due to this architecture the problem of query answering can also be broken into two steps. First, the query is decomposed, using the mediated mappings, into a set of elementary sub-queries expressed in terms of the application ontologies. Then, these sub-queries are rewritten, using the local mappings, in terms of their local sources schemas. This paper focus on a method for query processing that addresses the problem of efficient query answering. Our approach is illustrated by an example of a virtual store mediating access to online booksellers.

Metaphor is not merely a rhetorical device, characteristic of language alone, but rather a fundamental feature of the human conceptual system. A metaphor is understood by finding an analogy mapping between two domains. This paper argues that analogy mappings facilitate conceptual modeling by allowing the designer to reinterpret fragments of familiar conceptual models in other contexts. The contributions of the paper are expressed within the tradition of the Entity-Relation model.

A cooperative interface that, using suitable user models, alters the processing of the user's queries to include additional information that will block faulty inferences is described. In a sense, the interface actively teaches the user facts about the database not explicitly asked for. User interaction with the database then becomes a learning and discovery process guided by the queries posed to the interface. A semantics for user models is introduced that captures, with the help of default logic, the nonmonotonic behavior users normally exhibit. Results showing that the cooperative interface generates enough additional information to block all faulty inferences are presented.>

A modularization discipline for database schemas is first described. The dicipline incorporates both a strategy for enforcing integrity constraints and a tactic for organizing large sets of database structures, integrity constraints, and operations. A software tool that helps the development and maintenance of database schemas modularized according to the discipline is then presented. It offers a user-friendly interface that guides the designer through the various stages of the creation of a new module or through the process of changing objects of existing modules. The tool incorporates, in a declarative style, a description of the design and redesign rules behind the modularization discipline, hence facilitating the incremental addition of new expertise about database design.

Article On the correctness of a local storage subsystem Share on Authors: Marco A. Casanova View Profile , Arnaldo V. Moura View Profile , Luiz Tucherman View Profile Authors Info & Claims PODS '85: Proceedings of the fourth ACM SIGACT-SIGMOD symposium on Principles of database systemsMarch 1985 Pages 123–134https://doi.org/10.1145/325405.325420Published:25 March 1985 0citation56DownloadsMetricsTotal Citations0Total Downloads56Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Logical, algebraic, programming language, grammatical and denotational formalisms are investigated with res pect to their applicability to formal data base speci fication. On applying each formalism for the purpose that originally motivated its proposal, it is shown that they all have a fundamental and well integrated role to play in different parts of the specification process. An example is included to illustrate the methodological aspects.

View integration is investigated with the help of three classes of interrelational dependencies, inclusion dependencies, exclusion dependencies and union functional dependencies. The process of view integration is divided into two steps, combination and optimization. View combination consists in defining new interrelational dependencies that capture similarities between different views. The optimization step tries to reduce redundancy and the size of the schema. Finally, general results about interrelational dependencies are presented that lead to an optimization procedure for a restricted class of schemas.

A formal system is developed for reasoning about a class of dependencies that includes all classes considered in the literature. The usefulness of the system is illustrated by applying it to various database design problems. The system is shown to be sound and complete by adapting the analytic tableaux method of first-order predicate calculus to the class of dependencies adopted. Finally, the method is shown to be a decision procedure for the inference problem of a subclass of the dependencies considered.

Inclusion dependencies, or INDs (which can say, for example, that every manager is an employee) are studied, including their interaction with functional dependencies, or FDs. A simple complete axiomatization for INDs is presented, and the decision problem for INDs is shown to be PSPACE-complete. (The decision problem for INDs is the problem of determining whether or not Σ logically implies σ, given a set Σ of INDs and a single IND σ). It is shown that finite implication (implication over databases with a finite number of tuples) is the same as unrestricted implications for INDs, although finite implication and unrestricted implication are distinct for FDs and INDs taken together. It is shown that, although there are simple complete axiomatizations for FDs alone and for INDs alone, there is no complete axiomatization for FDs and INDs taken together, in which every rule is k-ary for some fixed k (and in particular, there is no finite complete axiomatization.) This is true whether we consider finite implication or unrestricted implication, and is true even if no relation scheme has more than three attributes. The nonexistence of a k-ary complete axiomatization for FDs and INDs taken together is proven by giving a condition which is necessary and sufficient in general for the existence of a k-ary complete axiomatization.

No abstract available.