Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.

The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) have been used for meta-data management on the Web. With the rapid development of Web-based applications, the growth of RDF(S) data is increasing dramatically. In the context of open Web, uncertainty naturally arises in RDF and RDFS (RDF(S) in short). To manage large-scale fuzzy RDF(S) data efficiently and effectively, we propose, in this paper, a fuzzy RDF(S) storage schema with fuzzy HBase databases (FHDBs). On the basis, we investigate how to query in FHDBs. We propose a set of query algorithms. We implement a prototype system to demonstrate the feasibility of our approach.

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object-oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

Vagueness (or imprecision) naturally arises in real-world Resource Description Framework (RDF) data. The way we query RDF data is a crucial subject because of the vagueness and the wide connectivity of such data. Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, there are a few works that have been led on RDF graph database. In this study, we dealt with a specific type of fuzzy quantified queries in the context of a (fuzzy) RDF graph database. We first show how these queries can be expressed and interpreted in a quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. A query processing strategy based on subgraph matching queries is also proposed. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is, enumerating all RDF isomorphism from the given RDF graph patterns into the data graph. The algorithm is based on a backtracking strategy, which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we perform some experiments to study its performances. The results of these experiments show that the method of dealing with fuzzy quantification in a query is feasible.

eXtensible Markup Language (XML) has been the de facto standard of data representation and exchange over the Web. In addition, imprecise and uncertain data are inherent in the real world. Although fuzzy data have been extensively investigated in the context of the relational model, the classical relational database model and its fuzzy extension to date do not satisfy the need of modeling complex objects with imprecision and uncertainty on the Web. On the basis of possibility theory, this paper concentrates on fuzzy information modeling in the fuzzy XML model and the fuzzy IFO model. In particular, the formal approach to mapping a fuzzy IFO model to a fuzzy document-type definition model is developed.

Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.

JSON is a simple, compact and light weighted data exchange format to communicate between web services and client applications. NoSQL document stores evolve with the popularity of JSON, which can support JSON schema-less storage, reduce cost, and facilitate quick development. However, NoSQL still lacks standard query language and supports eventually consistent BASE transaction model rather than the ACID transaction model. This is very challenging and a burden on the developer. The relational database management systems (RDBMS) support JSON in binary format with SQL functions (also known as SQL/JSON). However, these functions are not standardized yet and vary across vendors along with different limitations and complexities. More importantly, complex searches, partial updates, composite queries, and analyses are cumbersome and time consuming in SQL/JSON compared to standard SQL operations. It is essential to integrate JSON into databases that use standard SQL features, support ACID transactional models, and has the capability of managing and organizing data efficiently. In this article, we empower JSON to use relational databases for analysis and complex queries. The authors reveal that the descriptive nature of the JSON schema can be utilized to create a relational schema for the storage of the JSON document. Then, the powerful SQL features can be used to gain consistency and ACID compatibility for querying JSON instances from the relational schema. This approach will open a gateway to combine the best features of both worlds: the fast development of JSON, consistency of relational model, and efficiency of SQL.

The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a World Wide Web Consortium recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. In real-world applications, there are many practical situations where the descriptions of objects are accompanied by a degree of uncertainty. In an open Web environment, it is common case that the data from different sources may often contain inconsistent or imprecise information. In this paper, a fuzzy RDF data model is proposed. We present the syntax and semantics of fuzzy RDF data model. In particular, we investigate in the paper how to formally map fuzzy RDF data to relational databases.

Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.

This paper concentrates on modeling probabilistic events with fuzzy probability measures in relational databases and XML (Extensible Markup Language). Instead of crisp probability degrees or interval probability degrees, fuzzy sets are applied to represent imprecise probability degrees in relational databases and XML. A probabilistic XML model with fuzzy probability measures is introduced, which incorporates fuzzy probability measures to handle imprecision and uncertainty. In particular, the formal approach to reengineering the relational database model with fuzzy probability measures into the DTD (document type definition) model with fuzzy probability measures is developed in the paper.

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.

Extracting domain knowledge from databases can facilitate the development of Web ontologies. In this paper, a formal approach and an automated tool for constructing ontologies from Object-oriented database models (OODMs) are developed. The approach and tool can automatically translate an OODM and its corresponding database instances into the ontology structure and ontology instances, respectively. Case studies show that the approach is feasible and the automated construction tool is efficient.

Fuzzy ontologies are deemed as useful formalisms for dealing with vagueness in the Semantic Web community. Description logics (DLs) are the logical foundations of standard web ontology languages. Conjunctive queries are deemed as an expressive reasoning service for DLs. DL reasoners can be enriched by a conjunctive query service. In this study, we focus on fuzzy (threshold) conjunctive queries over knowledge bases encoding in fuzzy DL ALC(G), the well known fuzzy DL with customized fuzzy data type support. We provide a tableau-based algorithm for deciding query entailment of ALC(G). Our algorithm is applicable to more expressive DLs and arbitrary conforming fuzzy data type group.

XML has been the de-facto standard of information representation and exchange over the web. As the next generation of the Web language, XML is straightforwardly usable over the Internet. At the same time, the real world is filled with imprecision and uncertainty. However, the existed works fall short in their ability to model imprecise and uncertain data using XML. In this paper, we propose a new fuzzy XML data model based on XML Schema. With the model used, the fuzzy information in XML documents can be represented naturally. Along with the model, an associated algebra is presented formally. We also introduce how to use our algebra to capture queries expressed in XQuery. It shows that this model and algebra can establish a firm foundation for publishing and managing the histories of fuzzy data on the Web. © 2010 Wiley Periodicals, Inc.

In order to find all occurrences of a twig pattern in XML documents, a considerable amount of twig pattern matching algorithms have been proposed. At the same time, previous work mainly focuses on twig pattern query under the complete semantics. However, there is often a need to produce partial answers because XML data may have missing sub-elements. Furthermore, the existed works fall short in their ability to support twig pattern query under different semantics in fuzzy XML. In this paper, we study the problem of twig matches in fuzzy XML. We begin by introducing the extended region scheme to accurately and effectively represent nodes information in fuzzy XML. We then discuss the fuzzy query semantics and compute the membership information by using Einstein operator instead of Zadeh's min-max technique. On the basis, we propose two efficient algorithms for querying twig under complete and incomplete semantics in fuzzy XML. The experimental results show that our proposed algorithms can perform on the fuzzy twig pattern matching efficiently.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

Rules have been playing an increasingly important role in the Semantic Web. However, general rule languages are not capable of representing much imprecise and uncertain knowledge in the Semantic Web, nor are if-then rules. Combining if-then rules with OWL DL in the framework of fuzzy sets and possibility distribution, we propose fuzzy Semantic Web if-then Rule Language (f-SW-if-then-RL), and investigate its syntax and semantics. Considering nonmonotonicity, we employ unless rules to extend f-SW-if-then-RL, and f-SW-if-then-unless-RL appears. Two kinds of negation are introduced to express semantics of unless rules. And we extend rule interchange format R2ML to encode nonmonotonic fuzzy rules.

To deal with the problem of empty or too little answers returned from a Web database in response to a user query, this paper proposes a novel approach to provide relevant and ranked query results. Based on the user original query, we speculate how much the user cares about each specified attribute and assign a corresponding weight to it. This original query is then rewritten as an approximate query by relaxing the query criteria range. The relaxation order of all specified attributes and the relaxed degree on each specified attribute are varied with the attribute weights. For the approximate query results, we generate users' contextual preferences from database workload and use them to create a priori orders of tuples in an off-line preprocessing step. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at query time to expeditiously provide ranked answers. Results of a preliminary user study demonstrate that our query relaxation and results ranking methods can capture the user's preferences effectively. The efficiency and effectiveness of our approach is also demonstrated by experimental result.

Sensors are used to monitor some physical phenomena such as contamination, climate, building, and so on. The sensors collect and communicate their readings to the sensor databases for making decisions and answering various user queries. Due to continuous changes and possible errors in these values, the data values recorded in sensor databases may differ from the actual status. Queries using these values can yield incorrect and misleading answers. In order to manage the imprecision between the actual sensor value and the database value, the framework representing imprecision of sensor data has been proposed, in which each data value is represented as an interval. In this paper, we examine the situation when answer imprecision can be represented qualitatively and quantitatively. In particular, we propose two new kinds of imprecise queries called Top-k imprecise query and imprecise threshold query. Also, we investigate techniques for evaluating the qualitative and quantitative queries.

To deal with the problem of too many results returned from a Web database in response to a user query, this paper proposes a novel approach, which takes advantage of the contextual preferences to precompute a few representative orders of tuples and uses them to expeditiously provide ranked answers factoring in the information contained in the query. Contextual preferences take the form that item i1 is preferred to item i2 with an interest degree in the context of X. This paper formally defines contextual preferences, provides algorithms for creating tuple orders, clustering orders and processing queries, and presents experimental results to show their efficiency.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. However, information imprecision and uncertainty exist in many real-world applications. Thus constructing fuzzy ontology by extracting domain knowledge from fuzzy database model such as fuzzy ER model can profitably support fuzzy ontology development. In this paper, firstly, we give the formal definition and semantics of fuzzy ER model. Then, we introduce a kind of fuzzy extension of OWL DL, named fuzzy OWL DL. Furthermore, based on the fuzzy OWL DL, the formal definition and Model-Theoretic semantics of fuzzy OWL DL ontology are given. Whatpsilas more, we realize the formal translation from fuzzy ER model to fuzzy OWL DL ontology by a semantics-preserving translation algorithm. Finally, since a fuzzy OWL DL ontology is being equivalent to a description logic f-SHOIN(D) knowledge base, the reasoning problem of satisfiability, subsumption, and redundancy of fuzzy ER model may reason automatically through reasoning mechanism of f-SHOIN(D) is also investigated, which can contribute to constructing fuzzy OWL DL ontologys exactly that meet applicationpsilas needs.

Ontology is an important part of the W3C standards for the Semantic Web used to specify standard conceptual vocabularies to exchange data among systems, provide reusable knowledge bases, and facilitate interoperability across multiple heterogeneous systems and databases. However, current ontology is not sufficient for handling vague information that is commonly found in many application domains. A feasible solution is to import the fuzzy ability to extend the classical ontology. In this article, we propose a fuzzy ontology generation framework from the fuzzy relational databases, in which the fuzzy ontology consists of fuzzy ontology structure and instances. We simultaneously consider the schema and instances of the fuzzy relational databases, and respectively transform them to fuzzy ontology structure and fuzzy RDF data model. This can ensure the integrality of the original structure as well as the completeness and consistency of the original instances in the fuzzy relational databases.

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility-based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.

Computer applications in the nontraditional area have put requirements on conceptual data modeling. Some conceptual data models, being the tools of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and design of databases. In this paper, based on fuzzy set and possibility distribution theory, different levels of fuzziness will be introduced into the IFO data model, and the corresponding graphical representations are given. IFO data model is then extended to the fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.