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.

This paper describes the steps taken to construct a Natural Language interface based on Large Language Models (LLMs) for Busca360, an application in production at an energy company. The paper analyses how technologies developed for text-to-SQL perform on the Busca360 database and proposes solutions to overcome the limitations observed. The analysis is based on a benchmark that uses the Busca360 database and a set of 100 Natural Language (NL) questions and their ground truth SQL translations. The paper first investigates how text-to-SQL strategies perform on the Busca360 relational schema. The experiments showed that the performance of the strategies tested was significantly less than that reported in the leaderboards, mainly because schema linking failed. The Busca360 database was then equipped with views that rename the table and column names and have new columns that predefine joins that follow foreign keys. A second set of experiments using such views corroborated that schema linking largely improved, and the predefined joins reduced the complexity of the NL question translation. To enhance the results further, a third set of experiments tested a dynamic few-shot examples technique that prompted the LLM with examples of NL question/SQL translation, retrieved from a synthetic dataset pre-computed from the database. The paper concludes by outlining the current LLM-based Natural Language interface using views and the dynamic few-shot examples technique, and leveraging the Busca360 database keyword search services.

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 article describes experiments with a tool, called CourseObservatory, that applies sentiment analysis to comments made by students during course surveys. The main objective is to provide course coordinators and teachers with relevant information about the courses students take based on their qualitative feedbacks. The experiments use a dataset that contains comments in Portuguese found in questionnaires filled out by students from mid-2005 to the first semester of 2018, with a total of nearly 170,000 comments, after a cleaning process that removes blank comments. The experiments show that comments made by students are influenced by the final status achieved (approved or failed), among other facts.

Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience. Predictive models vary in terms of their amplitude, temporality and the learning algorithms employed. While amplitude refers to the ability of the model to operate on multiple degrees, temporality is often considered due to the natural temporal aspect of the data. In the absence of a comparative framework of learning algorithms, the aim of this paper has been to provide such an analysis, based on a proposed classification of strategies for predicting dropouts in Higher Education Institutions. Three different student representations are implemented (namely Global Feature-Based, Local Feature-Based, and Time Series) in conjunction with the appropriate learning algorithms for each of them. A description of each approach, as well as its implementation process, are presented in this paper as technical contributions. An experiment based on a dataset of student information from two degrees, namely Business Administration and Architecture, acquired through an automated management system from a university in Brazil is used. Our findings can be summarized as: (i) of the three proposed student representations, the Local Feature-Based was the most suitable approach for predicting dropout. In addition to providing high quality results, the Local Feature-Based representations are simple to build, and the construction of the model is less expensive when compared to more complex ones; (ii) as a conclusion of the results obtained via Local Feature-Based, dropout can be said to be accurately predicted using grades of a few core courses, so there is no need for a complex features extraction process; (iii) considering temporal aspects of the data does not seem to contribute to the prediction performance although it increases computational costs as the model complexity increases.

Higher Education Institutions store a sizable amount of data, including student records and the structure of a degree curriculum. This paper focuses on the problem of identifying how closely students follow the recommended order of the courses in a degree curriculum, and to what extent their performance is affected by the order they actually adopt. It addresses this problem by applying techniques to mine frequent itemsets to student records. The paper illustrates the application of the techniques for a case study involving over 60,000 student records in two undergraduate degrees at a Brazilian University.

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.

ISCOOL is an interactive educational game for text analysis and interpretation. It draws from several reference datasets, providing users with information about people, organisations and locations, as well as word definitions and historical facts that serve as the basis for reading comprehension and provide a wider context for information to be accessed, interpreted and understood. In game-play, users choose images to best illustrate a read text. The interactivity of the game encourages users to test their knowledge, and critically analyse what was read. ISCOOL was assessed by students with low literacy levels in English. The results show high levels of acceptance and applicability to genuine learning activities.

Understanding students' strengths and weaknesses can help in the design of teaching materials to successfully bridge identified gaps. Formal exams are useful to gauge the extent of learners' memorised information, but have been critiqued for not reflecting the true extent of learners knowledge. Diagnostic assessment assists teachers in setting task-specific plans and goals, for both individual students, and the learner-group as a whole. In this paper, we describe Treasure Explorers, a Game With a Purpose (GWAP) with a multilayered structure that facilitates the learning process, promotes user retention, and is designed to reward contributing players. A comprehensive evaluation based on game log and TAM model was conducted. The log of over 5,500 records reveals that aspects of engagement and learning had occurred, and qualitative evaluations show the applicability and usability of games as diagnostic assessment tools. Treasure Explorers assists teachers in identifying student difficulties, and has significant potential to help educators plan for lesson content.

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.

Abstract: Over the last ten years, members of the WEBIST community have dedicated their time and efforts to face a number of research challenges, making significant advances in Information Systems and pointing to new directions for innovation and learning. After ten successful WEBIST conferences and several scientific pub-lications, an extensive analysis of the WEBIST conferences has been carried out (involving authors, publi-cations, conference impact, topics coverage, community analysis and other aspects) to possibly assist us to further advance Information Systems. Thus, in this paper, we present an in-depth analysis of the last ten WEBIST conferences based on social network analysis, bibliometrics and statistical measures and describe a Web-based application built on top of triplified datasets to interactively explore the findings and possibly assist the Information Systems community to reveal new directions. 1

Forums play a key role in the process of knowledge creation, providing means for users to exchange ideas and to collaborate. However, educational forums, along several others online educational environments, often suffer from topic disruption. Since the contents are mainly produced by participants (in our case learners), one or a few individuals might change the course of the discussions. Thus, realigning the discussed topics of a forum thread is a task often conducted by a tutor or moderator. In order to support learners and tutors to harmonically align forum discussions that are pertinent to a given lecture or course, in this paper, we present a method that combines semantic technologies and a statistical method to find and expose relevant topics to be discussed in online discussion forums.

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.

This paper presents a text simplification method that transforms complex sentences into simplified forms. Our method uses NLP-techniques to simplify the text based on the target audience context, improving its overall understandability. We evaluate our approach in two aspects: grammatical structure and understandability. In both aspects, our approach achieved good results, showing its applicability to the learning process.

The proliferation of multimedia narratives has contributed to what is known as the "crisis of choice", which demands a much more active participation on the part of the user to consume multimedia content. To address this issue, a strategy is to offer users efficient search mechanisms, sometimes based on ontologies. However, one may argue that such mechanisms are often based on abstractions that do not adequately capture the essential aspects of multimedia narratives. This paper proposes a conceptual model to specify multimedia narratives that overcomes this limitation. The model is based on the notion of event and is therefore called Nested Event Model (NEMo). The paper also includes a complete example to illustrate the use of the model.

Connectivity and relatedness of Web resources are two concepts that define to what extent different parts are connected or related to one another. Measuring connectivity and relatedness between Web resources is a growing field of research, often the starting point of recommender systems. Although relatedness is liable to subjective interpretations, connectivity is not. Given the Semantic Web's ability of linking Web resources, connectivity can be measured by exploiting the links between entities. Further, these connections can be exploited to uncover relationships between Web resources. In this paper, we apply and expand a relationship assessment methodology from social network theory to measure the connectivity between documents. The connectivity measures are used to identify connected and related Web resources. Our approach is able to expose relations that traditional text-based approaches fail to identify. We validate and assess our proposed approaches through an evaluation on a real world dataset, where results show that the proposed techniques outperform state of the art approaches.

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.

SUMMARY The process of transforming data stored in relational databases (RDBs) into sets of Resource Description Framework (RDF) triples is known as triplification or RDB2RDF. It consists of two consecutive operations, schema extraction and data conversion. Schema extraction is a process similar to creating an external schema, and contains a collection of database views. The data conversion is divided into two steps. The first step consists of deciding how to represent database schema concepts in terms of RDF classes and properties, defining an RDB2RDF mapping. The second step consists of the actual conversion of relational data to RDF data instances, based on the mapping previously defined. Although the schema extraction is very well understood, the data conversion operation is still murky. Indeed, the World Wide Web Consortium RDB2RDF Working Group has been working to define a standard language, called R2RML, to describe RDB2RDF mapping files. The definition of the R2RML, however, is still undergoing changes. In this paper, we introduce an Eclipse plug‐in that supports the entire conversion process. Its architecture takes into consideration the specificities of the triplification process by providing a modular structure that encapsulates the stable and well‐understood components separately from the volatile, change‐prone mapping strategies. The latter are accessible via a well‐defined interface to promote information hiding and separation of concerns and to facilitate evolution. Copyright © 2012 John Wiley & Sons, Ltd.

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.

Every day, millions of new images, videos and audios are uploaded to the web. However, unlike text-based content, audio and video objects cannot be indexed by search engines. Thus, much valuable multimedia content stay unreachable for a great majority of online users. To overcome this problem we introduce a technique that automatically generates semantically enhanced descriptions of audio and video objects. The goal is to facilitate indexing and retrieval of the objects with the help of traditional search engines. Basically, the technique automatically generates static Web pages that describe the content of the digital audio and video objects, organized in such a way as to facilitate locating segments of the audio or video that correspond to the descriptions. The technique is a mashup ofWeb services that also provides translation of the descriptions and semantic enhancement. We thoroughly analyzed the click-data comparing accesses to the digital content before and after the automatic generation of the descriptions. The outcomes suggest that the technique significantly improve the retrieval of items, not only in terms of visibility, but also brings down language barriers, by supporting multilingual access.

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.

This paper first proposes a multi-agent architecture to mediate access to data sources. The mediator follows the classical approach to process user queries. However, in the background, it post-processes query results to gradually construct matchings between the export schemas and the mediated schema. The central theme of the paper is an extensional schema matching strategy based on similarity functions. The paper concludes with experimental results that assess the quality of the matching strategy.

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.

Workflow management systems usually interpret a workflow definition rigidly. However, there are real life situations where users should be allowed to deviate from the prescribed static workflow definition for various reasons, including lack of information, unavailability of the required resources and unanticipated situations. Furthermore, workflow complexity may grow exponentially if all possible combinations of anticipated scenarios must be compiled into the workflow definition. To flexibilize workflow execution and help reduce workflow complexity, this paper proposes a dual strategy that combines a library of predefined typical workflows, of manageable complexity, with a planner mechanism capable of synthesizing more complex workflows, at execution time, tuned to the scenario in question. This dual strategy is motivated by the difficulty of designing emergency plans, modeled as workflows, which account for real life complex crisis or accident scenarios..

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.

A formal system for proving properties of programs accessing a database is introduced. Proving that a program preserves consistency of the database is one of the possible applications of the system. The formal system is a variant of dynamic logic and incorporates a data definition language (DDL) for describing relational databases and a data manipulation language (DML) whose programs access data in a database. The DDL is a many-sorted first-order language that accounts for data aggregations. The DML features a many-sorted assignment in place of the usual data manipulation statements, in addition to the normal programming language constructs.

A logic for a relational data manipulation language is defined by augmenting a known logic of programs with rules for two new statements: the relational assignment, which assign a relational expression to a relation, and the random tuple selection, which extracts an arbitrary tuple from a relation. The usual operations on relations-retrieve, insert, delete, update-are then defined as special cases of the relational assignment, and the for-each construct scanning a relation tuple by tuple is introduced with the help of the random tuple selection.

No abstract available.