Towards Controlled Query Evaluation for Incomplete First-Order Databases

An important goal of security in information systems is confidentiality. A confidentiality policy specifies which users should be forbidden to acquire what kind of information. A controlled query evaluation should enforce such a policy even if users are able to reason about a priori knowledge and the answers to previous queries. The following aspects are considered: formal models of confidentiality policies based on potential secrets or secrecies, user awareness of the policy instance, and enforcement methods applying either lying or refusal, or a combination thereof. Reconsidering previous work and filling the gaps, we comprehensively treat and compare the resulting 12 cases. Thereby, the assumed completeness of the information system is essentially used.

Modern software applications store their data in remote cloud servers. Users interact with these applications using web browsers or thin clients running on mobile devices. A key concern for these applications is the correctness of the actions that update the data store, which are triggered by user requests. Considering that modern applications store and manage data for millions (even billions) of users, misuse or loss of user data can have catastrophic consequences. In this paper, we focus on automated discovery of data store bugs in applications that use development frameworks that are RESTful, enforce the Model–View–Controller architecture, and use Object Relational Mapping libraries to manipulate data. We present a formal data model for data stores and data store manipulation in such applications. We have developed a framework for verification of data models via translation to First Order Logic (FOL), followed by automated theorem proving. Due to the undecidability of FOL, this automated approach does not always produce a conclusive answer. We investigate the use of many-sorted logic in data model verification in order to improve the effectiveness of this approach. Many-sorted logic allows us to specify type information explicitly, thus lightening the burden of reasoning about type information during theorem proving. Our experimental results demonstrate that our verification approach is scalable to real-world web applications and is able to detect bugs in them.

In this paper, we present a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of a rule-based component and a lexicon. The rule-based unit implements the conversion process, which is based on a linguistic analysis of a FOL sentence, and the lexicon provides lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-based expert system shell. The conversion process currently covers a restricted set of FOL formulas.

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

Controlled query evaluation enforces security policies for confidentiality in information systems. It deals with users who may apply background knowledge to infer additional information from the answers to their queries. For each query the correct answer is first judged by some censor and then – if necessary – appropriately modified to preserve security. In previous approaches, modification has been done uniformly, either by lying or by refusal. A drawback of lying is that all disjunctions of secrets must always be protected. On the other hand, refusal may hide an answer even when the correct answer does not immediately reveal a secret. In this paper we introduce a hybrid answer modification method that appropriately combines lying and refusal. We prove that the new method is secure under the models of known potential secrets and of known secrecies, respectively. Furthermore, we demonstrate that the combined approach can be more cooperative than uniform lying and uniform refusal, and enjoys the advantages of both.

As an extension of the traditional modal logic, the fuzzy first-order modal logic is discussed in this paper. A description of fuzzy first-order modal logic based on constant domain semantics is given, and a formal system of fuzzy reasoning based on the semantic information of models of first-order modal logic is established. It is also introduced in this paper the notion of the satisfiability of the reasoning system and some properties associated with the satisfiability are proved.

Chapter 3 - Plan Recognition Using Statistical–Relational Models

Many authors have noted that a number of English modal sentences cannot be formalized into standard first-order modal logic. Some widely discussed examples include “There could have been things other than there actually are” and “Everyone who’s actually rich could have been poor.” In response, many authors have introduced an “actually” operator @ into the language of first-order modal logic. It is occasionally noted that some of the example sentences still cannot be formalized with @ if one allows only actualist quantifiers, and embedded versions of these example sentences cannot be formalized even with possibilist quantifiers and @. The typical justification for these claims is to observe that none of the most plausible candidate formalizations succeed. In this paper, we prove these inexpressibility results by using a modular notion of bisimulation for first-order modal logic with “actually” and other operators. In doing so, we will explain in what ways these results do or do not generalize to more expressive modal languages.

The amount of data has increased exponentially as a consequence of the availability of new data sources and the advances in data collection and storage. This data explosion was accompanied by the popularization of the Big Data term, addressing large volumes of data, with several degrees of complexity, often without structure and organization, which cannot be processed or analyzed using traditional processes or tools. Moving towards Big Data Warehouses (BDWs) brings new problems and implies the adoption of new logical data models and tools to query them. Hive is a DW system for Big Data contexts that organizes the data into tables, partitions and buckets. Several studies have been conducted to understand ways of optimizing its performance in data storage and processing, but few of them explore whether the way data is structured has any influence on how quickly Hive responds to queries. This paper investigates the role of data organization and modelling in the processing times of BDWs implemented in Hive, benchmarking multidimensional star schemas and fully denormalized tables with different Scale Factors (SFs), and analyzing the impact of adequate data partitioning in these two data modelling strategies.

This paper investigates algebraic query languages on temporal databases. The data model used is a multidimensional extension of the temporal modules introduced in [WJS95]. In a multidimensional temporal module, every non-temporal fact has a timestamp that is a set of n-ary tuples of time points. A temporal module has a set of timestamped facts and has an associated temporal granularity (or temporal type), and a temporal database is a set of multidimensional temporal modules with possibly different temporal types. Temporal algebras are proposed on this database model. Example queries and results of the paper show that the algebras are rather expressive. The operations of the algebras are organized into two groups: snapshot-wise operations and timestamp operations. Snapshot-wise operations are extensions of the traditional relational algebra operations, while timestamp operations are extensions of first-order mappings from timestamps to timestamps. Multiple temporal types are only dealt with by these timestamp operations. Hierarchies of algebras are defined in terms of the dimensions of the temporal modules in the intermediate results. The symbol TALG k is used to denote all the algebra queries whose input, output and intermediate modules are of dimensions at mostm, n and k, respectively. (Most temporal algebras proposed in the literature are in TALG 1 .) Equivalent hierarchies TCALC m;n k are defined in a calculus query language that is formulated by using a first-order logic with linear order. The addition of aggregation functions into the algebras is also studied. This work was partly supported by the NSF grant IRI-9409769 and also by an ARPA grant, administered by the Office of Naval Research under grant number N0014-92-J-4038.

We define a natural notion of standard translation for the formulas of conditional logic which is analogous to the standard translation of modal formulas into the first-order logic. We briefly show that this translation works (modulo a lightweight first-order encoding of the conditional models) for the minimal classical conditional logic $\mathsf {CK}$ introduced by Brian Chellas in [3]; however, the main result of the article is that a classically equivalent reformulation of these notions (i.e., of standard translation plus theory of conditional models) also faithfully embeds the basic Nelsonian conditional logic $\mathsf {N4CK}$ , introduced in [11] into $\mathsf {QN4}$ , the paraconsistent variant of Nelson’s first-order logic of strong negation. Thus $\mathsf {N4CK}$ is the logic induced by the Nelsonian reading of the classical Chellas semantics of conditionals and can, therefore, be considered a faithful analogue of $\mathsf {CK}$ on the non-classical basis provided by the propositional fragment of $\mathsf {QN4}$ . Moreover, the methods used to prove our main result can be easily adapted to the case of modal logic, which makes it possible to improve an older result [10, Proposition 7] by S. Odintsov and H. Wansing about the standard translation embedding of the Nelsonian modal logic $\mathsf {FSK}^d$ into $\mathsf {QN4}$ .

Despite the popularity of the use of high-resolution parameter estimation for channel characterisation and modelling purposes, the influence of the antenna data model on robustness and accuracy of the estimation has been underexposed. The point to be addressed in this paper is that the use of an incomplete data model inherently will result in biased and artificially spread angular estimates. For some antenna array types, estimation bias is even unavoidable, irrespective of the used data model. It is not inconceivable that the popular approach of clustering multi-paths components for channel modelling is spurred by the artefacts resulting from data model choices as described in this paper. Practical examples are given for two popular types of arrays, the uniform linear and uniform circular array. (8 pages)

Sowa's conceptual structures work is broadly framed in terms of logic, linguistics, knowledge representation, conceptual analysis and mental models. Recently the CG representation has been proposed as a normative language for metalevel descriptions of traditional data models by the ANSI Information Resource Directory System (IRDS) working group (X3H4). This paper explores some issues that arise from attempts to use ontologies and “crystallized” representations to perform such things as data model translation and integration. Issues and alternate views that grow out of Sowa's knowledge soup metaphor are described and a functional architecture is proposed that addresses some of these issues. The notions of model knowledge as a dynamic and cognitive construction based on contexts is developed and combined with the idea of global workspaces.KeywordsData ModelConceptual SchemaConceptual StructureCognitive AgentFunctional ArchitectureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We generalize two well-known model-theoretic characterization theorems from propositional modal logic to first-order modal logic (FML, for short). We first study FML-definable frames and give a version of the Goldblatt–Thomason theorem for this logic. The advantage of this result, compared with the original Goldblatt–Thomason theorem, is that it does not need the condition of ultrafilter reflection and uses only closure under bounded morphic images, generated subframes and disjoint unions. We then investigate Lindstrom type theorems for first-order modal logic. We show that FML has the maximal expressive power among the logics extending FML which satisfy compactness, bisimulation invariance and the Tarski union property.

A data model for moving objects is a blueprint that describes data of interest related to movements of objects and a set of operations represented in an information or database management system. Designing an appropriate data model fosters conceptual understanding of the dynamic phenomena of moving objects, yields efficient data storage and retrieval, and further facilitates spatiotemporal analytical processes. Various data models have been proposed to efficiently and effectively store, manage, query, and analyze data about moving objects. This entry starts with the description of fundamental elements of moving objects and their abstract data types. Then it reviews data modeling approaches for representing moving objects, including conventional spatiotemporal data model, constraint model, moving object data model, semantic trajectory data model, event‐based data model, and models for handling big moving object data.