Theorem prover approach to semistructured data design

Various works have been done in verifying object constraint language (OCL) by translating it to formal specification language. This paper presents the process of translating OCL to prototype verification system (PVS) using model transformation. PVS is a formal specification language based on higher-order logic and comes with a theorem prover. Two processes have been identified to translate OCL to PVS. The first process translates OCL constraint to PVS specification directly using model-to-text transformation while the second process transforms OCL to PVS model. Both processes are evaluated to identify the advantages and disadvantages. Transforming OCL to PVS model allow the result to be use for other model management operation.

Systems for managing and querying semistructured-data sources often store data in proprietary object repositories or in a tagged-text format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistructured data instance is given, a STORED mapping can be generated automatically using data-mining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a document-type-descriptor (DTD), when present, can be exploited to further improve performance.

Before defining a model for uncertain and imprecise semistructured data, a clear understanding of the semantics of ignorance is necessary. We show that many dimensions of ignorance may coexist inside a semistructured data instance. We classify them, on the basis of a general data model, and we identify their interdependencies. We also show that the restriction of our model to the relational context leads to only one dimension of ignorance, as revealed in many other scientific works.

Semistructured data has become prevalent with the growth of the Internet. The development of new web applications that require efficient design and maintenance of large amounts of data makes it increasingly important to design good semistructured databases to prevent data redundancy and updating anomalies. However, it is not easy, even impossible, for current semistructured data models to capture the semantics traditionally needed for designing databases. In this paper, we show how an object-relationship-attribute model for semistructured data (ORA-SS) can facilitate the design of good semistructured databases. This is accomplished via the normalization of ORA-SS. An XML DTD or Schema generated from a normal form ORA-SS schema diagram has no undesirable redundancy, and thus no updating anomalies for the complying semistructured databases. The general design methodology and detailed steps for converting an ORA-SS schema diagram into a normal form ORA-SS schema diagram are presented. These steps can also be used as guidelines for designing semistructured databases using the ORA-SS model.

Semistructured data has attracted much attention in database research lately. New data models, query languages and systems have been developed. However, very little research has been done on formally defined semantics for the new data models and query languages. As a consequence, some aspects of the meaning of expressions in the database and answers to queries are not completely understood. In this paper we propose a semantics for semistructured data based on a modal logic framework used to give semantics to feature structures. A semistructured database is seen as inducing a certain model, which is then used to evaluate queries posed to the database in a first-order language. The main advantage of this approach is to give a formal basis for analyzing some possibly conflicting views of semistructured data as a model for partial information.KeywordsModal LogicQuery LanguageComplex ObjectOutgoing EdgeHybrid LogicThese 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.

In recent years, Architecture Analysis & Design Language (AADL) has been applied to the development of dependable real-time systems, in which the quality of the developed software is an important factor. Thus, to make sure that real-time systems are really dependable, we must verify the important properties, such as safety and reliability. This paper describes a contribution to the transformation of AADL models. In this paper we present an approach for transforming AADL model into Prototype Verification System (PVS) specification and deductive verification of such AADL models using the PVS interactive theorem prover. Our transformation includes two aspects: structure and behavior description. Applying a PVS specification of a AADL language semantics, we generate a formal representation of the AADL model. After the transformation, some properties, such as safety and reliability, of the model build by AADL could be verified using PVS. We use an example to demonstrate the correctness and feasibility of our approach. Then, we verified the safety and reliability attributes of dependability in our model. At last, we conclude our paper by proposing that model transformation provides powerful support to improve the integration of formal verification in an industrial engineering process and we presented our future work direction.

Cloud computing enables one to access applications through Internet, without explicitly installing them on one single computing device. Formal modeling and verification of cloud services and their composition is fundamental to guarantee the accurate working and availability of cloud applications. In this paper, we use the Prototype Verification System (PVS) for formal modeling and verification of cloud services. Cloud services are modeled in PVS with the combination of logical predicates that represent the relations among the timed data sequences being observed at its input and output ports. Furthermore, composition operators are modeled that can be used to combine different cloud services together to construct more complex services. With our approach, properties of cloud services and refinement relations between different cloud services can be formalized as goals and proved with the PVS proof assistant.

Verifying safety properties of a nonlinear control by interactive theorem proving with the Prototype Verification System

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.

This paper presents a formally verified decision procedure for determinining the satisfiability of a system of univariate polynomial relations over the real line. The procedure combines a root counting function, based on Sturm's theorem, with an interval subdivision algorithm. Given a system of polynomial relations over the same variable, the decision procedure progressively subdivides the real interval into smaller intervals. The subdivision continues until the satisfiability of the system can be determined on each subinterval using Sturm's theorem on a subset of the system's polynomials. The decision procedure has been formally verified in the Prototype Verification System (PVS). In PVS, the decision procedure is specified as a computable boolean function on a deep embedding of polynomial relations. This function is used to define a proof producing strategy for automatically proving existential and universal statements on polynomial systems. The soundness of the strategy solely depends on the internal logic of PVS.

Regarding the construction of the “State Grid Cloud” platform, various businesses of the power grid have their own Wed systems. The data between different websites is scattered and the coupling between resources is low, which can easily form the problem of information islands. This paper is oriented to the semi-structured HTML text data in web pages under the State Grid cloud architecture platform, and uses the Python-based Scrapy framework to collect semi-structured power data information from various power business websites. We propose a semi-structured text data classification model based on BIGRU neural network and Bayesian classifier. BIGRU neural network is used to extract text features, Bayesian classifier is used for classification, and the TF-TDF algorithm is used to assign weights to improve the traditional recurrent neural network model with many parameters and long training model time. We use this method to simulate the semi-structured HTML text data of the State Grid, and conduct a comparative experiment with the traditional neural network model. The experimental results show that the classification algorithm can effectively improve the efficiency and accuracy of power semi-structured text data classification.

The Ailaoshan Orogen in the southeastern Tibet Plateau, situated between the Yangtze and Simao blocks, underwent a complex structural, magmatic, and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental concentrations. These elements can conceal or reduce anomalies due to the mutual effect between different anomaly areas. Dividing the whole zone into subzones based on tectonic settings, ore cluster areas, or sample catchment basins (Scb), geochemical and structural anomalies associated with gold (Au) mineralization have been identified utilizing mean plus twice standard deviations (Mean + 2STD), factor analysis (FA), concentration‐area (C‐A) modeling of stream sediment geochemical data, and lineament density in both the Ailaoshan Orogen and the individual subzones. The FA in the divided 98 Scbs with 6 Scbs containing Au deposits can roughly ascertain unknown rock types, identify specific element associations of known rocks and discern the porphyry or skarn‐type Au mineralization. Compared with methods of Mean + 2STD and C‐A model of data in the whole orogen, which mistake the anomalies as background or act the background as anomalies, the combined methods of FA and C‐A in the separate subzones or Scbs works well in regional metallogenic potential analysis. Mapping of lineament densities with a 10‐km circle diameter is not suitable to locate Au deposits because of the delineated large areas of medium‐high lineament density. In contrast, the use of circle diameters of 1.3 km or 1.7 km in the ore cluster scale delineates areas with a higher concentration of lineament density, consistent with the locations of known Au deposits. By analyzing the map of faults and Au anomalies, two potential prospecting targets, Scbs 1 and 63 with a sandstone as a potential host rock for Au, have been identified in the Ailaoshan Orogen. The use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying mineralization anomalies associated with Au, rather than analyzing the entire large area.

Archaeological data repositories usually manage excavation data collections as project-level entities with restricted capacities to facilitate search or aggregation of excavation data at the sub-collection level (trenches, finds, season reports or excavation diaries etc.). More granular access to excavation data collections would enable layered querying across their informational content. In the past decade, several attempts to adapt CIDOC CRM in order to provide more explicit descriptions of the excavation universe have resulted in the use of domain-specific model extensions (e.g. CRMarchaeo, CRMsci, CRMba). Each focuses on corresponding aspects of the excavation research process, while their combined usage has potential to support expressive data mappings at the sub-collection level. As part of the ARIADNEplus project, several CIDOC CRM developers and domain experts have collaborated to undertake conceptual mapping exercises, to address the practicalities of bringing excavation data descriptions together and to link these to our overall aspirations in terms of excavation data discoverability and reusability. In this contribution, we discuss the current state and future directions of the field of semantic representation of archaeological excavation data and consider several issues that constrain the applicability of existing solutions. We identify five key enabling technologies or research areas (Conceptual models and semantic data structures, Conceptual modelling patterns, Data mapping workflows and tools, Learning technologies and Semantic queries) and assign readiness levels to assess their level of technological maturity. Our research demonstrates that while the existing models and domain-specific extensions are deemed adequate, there is a need for more user-friendly methods and tools to structure data in meaningful and interoperable ways. The next steps involve consolidating relevant semantic structures, improving modelling implementation guidance, adhering to consistent workflows, developing engaging curricula, and documenting real-case examples to demonstrate the benefits and results of semantic data integration.

One of the challenges for designing multi-agent systems is how to capture and reason about agent knowledge and knowledge evolution in a highly abstract and modular way. Hence it is very desirable to have a generic framework in which such systems can be conveniently specified and the properties verified under one umbrella. As a classical reasoning support, the model checking technique has proved to be applicable for systems of reasonable size. However current model checkers for epistemic logics suffer from the state explosion problem and their inability to handle infinite state problems. Prototype Verification System (PVS) is an environment for the development of formal specifications. It integrates a highly expressive specification language and a well supported theorem prover. In this paper, we demonstrate our attempt towards mechanizing epistemic logic reasoning by building a formal, sound and complete verification framework in PVS for reasoning about a spectrum of (dynamic) epistemic logics.KeywordsModal LogicMultiagent SystemProof SystemKripke ModelReasoning SystemThese 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.

An object wrapper [3,4] is a piece of middleware that provides an object view on top of a legacy database management system. But legacy database management systems such as relational database systems (RDS) and multidimensional database systems (MDS) [5,6,7] are different in many aspects from an Object-oriented Data Model. Among these aspects are semantic management, data structures and data types supported in these models. As a result, it is crucial to understand these data models in order to design and implement an efficient piece of middleware. In this paper, a short discussion of the real-world data modelling requirements is presented. Then, this is followed by a discussion on an Object View’s requirements. The third section of the paper presents the architecture of the Object-to-Multidimensional Mapping Engine (O-MME) [10,11,12,13,14,15,16,17,18,19]. A detail discussion on major interfaces and classes in the Object-to-Multidimensional mapping algorithm are presented next. Issues such as data type conversion and database language statements generation are discussed in this paper also. Finally, some performance issues are presented and future possible researches are outlined.KeywordsStructure Query LanguageMapping EngineCache ManagerBusiness ClassisRelational Database SystemThese 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.