TALE — A Temporal Active Language and Execution model

The ongoing OMG standard on the “Semantics of a Foundational Subset for Executable UML Models” identifies a subset of UML (called fUML, for Foundational UML), for which it defines a general-purpose execution model. This execution model therefore captures an executable semantics for fUML, providing an unambiguous basis for various kinds of model-based exploitations (model transformation, code generation, analysis, simulation, debugging etc.). This kind of facility is of great interest for the domain of real time systems, where analysis of system behavior is very sensible. One may therefore wonder if the general-purpose execution model of fUML can be used to reflect execution semantics concerns of real-time systems (e.g., concurrency, synchronization, and scheduling.). It would practically mean that it is possible to leverage on this precise semantic foundation (and all the work that its definition implied) to capture the precise execution semantics of real-time systems. In this paper, we show that this approach is not directly feasible, because of the way concurrency and asynchronous communications are actually handled in the fUML execution model. However, we show that introducing support for these aspects is technically feasible and reasonable in terms of effort and we propose lightweight modifications of the Execution model to illustrate our purpose.KeywordsfUMLMDDModel SimulationConcurrent systemsReal-time systems

A novel execution model for rule application in active databases is developed and applied to the problem of updating derived data in a database represented using a semantic, object-based database model. The execution model is based on the use of “limited ambiguity rules” (LARs), which permit disjunction in rule actions. The execution model essentially performs a breadth-first exploration of alternative extensions of a user-requested update. Given an object-based database schema, both integrity constraints and specifications of derived classes and attributes are compiled into a family of limited ambiguity rules. A theoretical analysis shows that the approach is sound: the execution model returns all valid “completions” of a user-requested update, or terminates with an appropriate error notification. The complexity of the approach in connection with derived data update is considered.

Article Free Access Share on Processing queries for first-few answers Authors: Roberto J. Bayardo Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile , Daniel P. Miranker Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 45–52https://doi.org/10.1145/238355.238372Online:12 November 1996Publication History 24citation256DownloadsMetricsTotal Citations24Total Downloads256Last 12 Months4Last 6 weeks1 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 SiteeReaderPDF

A novel execution model for rule application in active databases is developed and applied to the problem of updating derived data in the context of semantic, object-based database models. The execution model is based on the use of "limited ambiguity rules" (LARs), which permit disjunction in rule actions. The execution model essentially performs a breadth-first exploration of alternative extensions of a user-proposed update, and returns all "completions" of that update, where a "completion" is defined to be an extension of a user-proposed update that satisfies a family of natural conditions (e.g., that no constraints are violated). Given a semantic, object-based database model schema, integrity constraints as well as specifications of derived classes and attributes are compiled into a family of LARs. A formal proof of the correctness of the system is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The article examines the features of the use of modern information systems and technologies in the activities of enterprises in order to increase the efficiency of management decisions. Based on a study of modern corporate practices in the global financial market, it is proved that corporate information systems for the management of medium and large enterprises is a necessary tool to minimize costs and increase profitability. The purpose of the article is to research the features of modern information systems and technologies using in activity of the enterprises for the purpose of increase of efficiency of administrative decisions. Information system as a set of organizational and technical means for storing and processing information in order to meet the information needs of users, which provides the collection, retrieval, processing and transmission of information. To solve the existing problems of corporate information systems development, an algorithm of actions to increase the level of informatization and efficiency of corporations, which will address current issues of corporate information systems, taking into account domestic legislation and features of corporate governance, increase efficiency and transparency of business processes and provide centralized support for management decisions. at all levels and in all areas of business companies. It is emphasized that one of the main properties of information systems is divisibility into subsystems, which has scales in terms of its development and operation. The stages of creation and functioning (life cycle) of information are specified. It is proved that the use of information systems is a prerequisite for a modern enterprise, that will promote the adoption of sound strategic management decisions aimed at increasing competition (capacity and strengthening the economic security of the enterprise. An effective area for improvement management of the enterprise is implemented modern information systems and technologic, which makes it possible to increase the speed,quality and reliability of collection, storage and information processing; significantly reduce management line staff of the enterprise, which there is preparation of information for the form management decisions; print in the right time management and administration line staff of the enterprise of high-quality other formation; timely and efficient analysis and forecasting of economic activity under receptions; make decisions quickly and efficiently at all levels of enterprise management.

Framed temporal logic programming

In general, the coupling modes may divide into immediate, deferred and detached modes in active database systems, but this classification doesn't suffer to clearly separate various execution models in concurrent active rule system. The ambiguity had been exploited that the same coupling modes (e.g. immediate) have different semantics in various active database systems. To clear up this ambiguity, we classified the coupling modes into syn-coupling and asyn-coupling modes, according to the key issue, synchrony/asynchrony, in parallel programming language. Rule execution semantics for various coupling modes are distinctly defined. It is beneficial to implementation and usage of the active rule system. After graph-based rule system (E-RG) and its execution model, we show the various strategies to construct the syn-coupling and asyn-coupling modes in E-RG rule system, based on semantics for coupling modes.

This paper considers a model for the task of controlling relations with clients by estimating the stability of sales. A procedure is suggested for building a system for supporting decision-making about the main parameters of the task—a trend model, an analyzed period, and the number of such periods. The efficiency of the suggested procedure is demonstrated on real data from the point of view of the prospects of its creation in modern corporative information systems.

One of the modern trends in the development of information and communication technologies that have evolved into the Internet of Things (IoT) paradigm is the progressive idea of using blockchain technology in IoT. This trend is described in a large number of modern literature sources, which point out the significant advantages of using blockchain, primarily related to decentralization, immutability, transparency and security. This allows us to conclude that the implementation of blockchain technology in IoT information and communication systems is highly relevant. However, the authors of different studies describe significant problems, including the problem of scalability of the blockchain itself, as well as the problems of energy efficiency and limited resources in IoT devices. Due to these problems, the implementation of blockchain technology in the Internet of Things is becoming a challenge and determines the relevance and feasibility of researching this topic and finding effective solutions related to the integration of two very powerful technologies in modern information and communication systems. The article presents an analytical overview of the issues of integrating blockchain technology into IoT, in particular, the use of the Proof-of-Work (PoW) consensus algorithm in IoT and the use of alternative blockchain protocols, including IOTA, Ethereum Light Client and Litecoin, which allow increasing the efficiency of performing tasks in IoT infrastructure, taking into account the limited resources of devices; existing problems and implementation methods, such as the use of light clients and proxy servers. The article also considers the feasibility of using a full blockchain node on IoT devices and ways to increase system efficiency by pre-processing data before sending it to the blockchain network. The article also highlights the problem of insufficient implementation of blockchain technology in various spheres of human life, as well as the search for the reasons for this problem. The results of the analytical review make it possible to substantiate the purpose and advantages of integrating blockchain technology into the Internet of Things, as well as to summarize the main issues and substantiate a number of tasks for relevant and progressive scientific research in the field of modern information and communication systems.

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

A City Metaphor to Support Navigation in Complex Information Spaces

The aim of this work is to present a daily simulation model for technical and economical analysis of natural gas microturbine application for residential complex. From the energy generation side, the model is based on the microturbine electrical and thermal efficiency, and on the boiler, heat exchanger and chiller equations. On the other hand, from the load side, the model is based on consumer electrical and thermal load curves. In this way, simulation for different scenarios can be made, and their results may be used as decision support systems by investors. The main advantage of this model is the timeframe that is used, which is flexible, varying from hours, days, weeks, up to years. Therefore, the work granularity allows take into consideration details that conventional methodology does not. In this paper is given details of the model, and simulation results are provided to show it feasibility.

The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.

Biomedical Named Entity Recognition (BioNER) could be found in high-quality annotated biomedical dataset of some applications such as medical question answering, clinical documents classification and decision support system. However, high-quality biomedical documents (i.e., PubMed, MPlus) as the main source of BioNER dataset is only available in English while it is lack in Indonesian. Efforts to annotate such documents is also burdensome since it requires extensive work of experts. Transformers based model, i.e. BERT and pretrained multilingual language models lead to an opportunity to perform crosslingual transfer learning from well progressed English BioNER to Indonesian language. This paper investigates XLM-Roberta and M-BERT as pretrained multi-lingual model to perform BioNER for Indonesian biomedical corpora. The model is fine-tuned in English documents before being evaluated in Indonesian biomedical test data. As the results, XLM-Roberta achieves better than M-BERT model in all measurements metrics. The investigations also compare the performance of multilingual with monolingual language model to evaluate the BioNER task and found no significant result difference between both models.

Large language models’ domain-specific capabilities can be enhanced through specialized datasets, yet constructing comprehensive cybersecurity datasets remains challenging due to the field’s multidisciplinary nature. We present PenQA, a novel instructional dataset for penetration testing that integrates theoretical and practical knowledge. Leveraging authoritative sources like MITRE ATT&amp;CK™ and Metasploit, we employ online large language models to generate approximately 50,000 question–answer pairs.We demonstrate PenQA’s efficacy by fine-tuning language models with fewer than 10 billion parameters. Evaluation metrics, including the BLEU, ROUGE, and BERTScore, show significant improvements in the models’ penetration testing capabilities. PenQA is designed to be compatible with various model architectures and updatable as new techniques emerge. This work has implications for automated penetration testing tools, cybersecurity education, and decision support systems. The PenQA dataset is available in our GitHub repository.