Source Schema Research Articles

A Survey of Bigdata Analysis, Extracting Data and Mapping the Data

Data mapping is one of the simplest terms is to map source data fields and their related target data fields. Mapping can have a varying degree of complexity, depending on the number, data types, schema, primary keys, and foreign keys of the data sources. Nowadays, Archaeological research is based on an interdisciplinary approach which makes use of a wide range of technologies allowing for the collection of data and information about sites and archaeological findings. The purpose of archaeology is to learn more about past societies and the development of the human race. An essential part of the archaeological data is related to spatial information that links historical contents to the metric reconstruction of monuments and artifacts, and show their mutual relations in a map. A critical a part of the archaeological records is associated with spatial data that links ancient contents to the metric reconstruction. By processing a steady stream of all real-time data, organizations can make time-sensitive decisions faster than ever before, monitor emerging trends, course-correct rapidly and jump on new business opportunities. To design a data mapping framework process, the data from various sources uses a new proposed technique. To secure the high profile raw and analyzed data using the combination of hardware and software any key generation for data extraction and mapping. The information can be accessed only through the authenticated source of the framework and hence duplication and data theft is extremely difficult. This paper follows the various data mapping techniques handled in previous work and also shows the limitations of existing techniques.

Querying Data Exchange Settings Beyond Positive Queries

Abstract Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early work, but little attention has been paid to more general queries. A few proposals of semantics for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings, for example, with weakly-acyclic dependencies. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is $\text{co}\text{NP}\text{-complete}$ when the dependencies are weakly-acyclic. Moreover, in the latter case, we show that exact answers under our semantics can be computed by means of logic programs with choice, thus exploiting existing efficient systems. For more efficient computations, we also show that our semantics allows for the construction of a representative target instance, similar in spirit to a universal solution, that can be exploited for computing approximate answers in polynomial time.

Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.

Metacognitive differentiation of item memory and source memory in schema-based source monitoring.

Item memory and source memory are different aspects of episodic remembering. To investigate metamemory differences between them, the authors assessed systematic differences between predictions of item memory via Judgments of Learning (JOLs) and source memory via Judgments of Source (JOSs). Schema-based expectations affect JOLs and JOSs differently: Judgments are higher for expected source-item pairs (e.g., "nightstand in the bedroom") than unexpected pairs (e.g., "bed in the bathroom"), but this expectancy effect is stronger on JOSs than JOLs (Schaper et al., 2019b). The current study tested theoretical underpinnings of this difference. Due to semantic priming, JOLs should be influenced by the consistency between an item and any of the schemas activated at study. JOSs, however, should be influenced by the (in)consistency between an item and its actual source. In three experiments, source-item pairs varied in strength of consistency and inconsistency. Participants provided item-wise JOLs and JOSs. Regardless of an items' actual source, JOLs were higher the more consistent an item was with any of the source schemas, but only if that schema was activated by occurring as a source at study. JOLs were also biased by the actual source: JOLs were lower the more inconsistent an item was with its actual source. By contrast, JOSs were primarily influenced by an item's (in)consistency with its actual source (positively for consistency, negatively for inconsistency). Thus, participants metacognitively differentiated item memory and source memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ForBackBench

The problems of Data Integration/Exchange (DE) and Ontology Based Data Access (OBDA) have been extensively studied across different communities. The underlying problem is common: using a number of differently structured data-sources mapped to a mediating schema/ontology/knowledge-graph, answer a query posed on the latter. In DE, forward-chaining algorithms, collectively known as the chase, transform source data to a new materialised instance that satisfies the ontology and can be directly queried. In OBDA, backward-chaining algorithms rewrite the query over the source schema, taking the ontology into account, in order to execute the rewriting directly on the sources. These two reasoning approaches have seen an individual rise in algorithms, practical implementations, and benchmarks. However, there has not been a principled methodology to compare solutions across both areas. In this paper we provide an original methodology and a benchmark infrastructure - a set of test scenarios, generator and translator tools, and an experimental infrastructure - to allow the translation and execution of a DE/OBDA scenario across areas and among different chase and query-rewriting systems. In the process, we also present a syntactic restriction of linear Tuple Generating Dependencies that precisely captures DL-Lite R , a correspondence previously uninvestigated. We perform cross-approach experiments under a wide range of assumptions, such as the use of different source-to-target mapping languages, shedding light to the interplay between forward-and backward-chaining. Our preliminary results show that, indeed, chase can compete and might overcome query rewriting even in the face of large data especially for complex mapping languages.

Schema mapping coverage

In this paper, we introduce and study schema mapping coverage for relational databases. Given a relational schema mapping in the presence of both source dependencies and target dependencies, the coverage problem is to decide and describe which source instances have solutions under the mapping. Our main motivation is to describe limitations of schema mappings and hence to effectively determine if the mappings can fulfill their user expectations for the given tasks. We first propose using database dependencies to model user expectations of schema mappings. Then we formally define the notion of schema mapping coverage and propose using a set of dependencies in the source schema language to represent coverage. We look into how target dependencies indirectly enforce dependencies via mapping specifications on source instances and thus determine which source instances have solutions. We prove that the problem of computing schema mapping coverage is undecidable in general. We present algorithms for computing coverage for schema mappings where both the source dependencies and the target dependencies consist of functional dependencies and acyclic inclusion dependencies. Schema mapping coverage describes the ability to map source instances to target instances for mappings, it provides a way to describe limitations of schema mappings, which can be especially useful for mapping design and evolution.

Providing accurate answers to OLAP queries based on standardized moments of data cubes

In this paper, we focus on the problem of providing accurate estimates to a target data cube from sets of source data cubes, which share the same summary measures. We investigate the acyclic and cyclic schemas of data sources and show that the more accurate target data cube can be computed on the basis of third and fourth standardized moments (i.e., skewness and kurtosis, respectively) of the source data cubes. In particular, we consider the mean absolute deviation of these parameters over the data cubes involved in the sets. For both standardized moments, we prove formally the set that achieves higher mean of skewness/kurtosis shows lower mean absolute deviation of skewness/kurtosis. On the basis of these results, we prove the set of data cubes with the lower mean absolute deviation of skewness/kurtosis achieves more accurate target estimate. We show that groups with cyclic schemas that present lower mean absolute deviation of standardized moments, achieve more accurate target estimate with respect to the ones with acyclic schemas as well. We define an algorithm that identifies within each sets of acyclic and cyclic groups, the one that achieves more accurate estimate for the target data cube on the basis of standardized moments. We provide some experimental results in order to verify our theoretical results.

Exploiting Declarative Mapping Rules for Generating GraphQL Servers with Morph-GraphQL

In the last decade, REST has become the most common approach to provide web services, yet it was not originally designed to handle typical modern applications (e.g. mobile apps). GraphQL was proposed to reduce the number of queries and data exchanged in comparison with REST. Since its release in 2015, it has gained momentum as an alternative approach to REST. However, generating and maintaining GraphQL resolvers is not a simple task. First, a domain expert has to analyze a dataset, design the corresponding GraphQL schema and map the dataset to the schema. Then, a software engineer (e.g. GraphQL developer) implements the corresponding GraphQL resolvers in a specific programming language. In this paper, we present an approach to exploit the information from mappings rules (relation between target and source schema) and generate a GraphQL server. These mapping rules construct a virtual knowledge graph which is accessed by the generated GraphQL resolvers. These resolvers translate the input GraphQL queries into the queries supported by the underlying dataset. Domain experts or software developers may benefit from our approach: a domain expert does not need to involve software developers to implement the resolvers, and software developers can generate the initial version of the resolvers to be implemented. We implemented our approach in the Morph-GraphQL framework and evaluated it using the LinGBM benchmark.

Translation of Metaphor from the Perspective of Cognitive Linguistics

Metaphor not only contains the characteristic connotation of the language itself, but also the words that cannot be expressed in a straightforward manner. The traditional metaphor view holds that metaphor is only used to beautify and modify language, but cognitive linguistics regards metaphor as a mapping of thinking, mainly through people’s existing experience and knowledge to understand new things, that is to say, from a known concept domain to understand another unknown concept domain. Therefore, metaphor can be regarded as a medium for establishing connection between the source domain and the target domain, and it also plays an important role in our study and daily life. This paper analyzes the translation of metaphor from the perspective of cognitive linguistics. Through the translation of various metaphors, we find that in the process of metaphor translation, we need to distinguish the source schema from the image schema metaphor; also it is necessary to distinguish the type of text; as well as the cultural meaning behind the text and the value to be conveyed. The translation strategy of direct translation or free translation, naturalization or alienation is used to translate the target domain.

A Study on Information-Preserving Schema Transformations

The problem of determining the relative information capacity between two knowledge bases or schemas, of the same or different models, is inherent when implementing schema transformations. When restructuring one schema into another, one expects that the schema transformation supports the complete and correct mapping of all the information contents from the source schema to the target schema. Such a characteristic is commonly referred to as information capacity preservation or schema dominance. This paper presents a formal and constructive approach to measure the relative information capacity, in the restricted case of first-order schemas related by first-order mappings. It complements the existing definitions of information capacity preservation from the perspective of model theory, showing the exact relationships among the constraints of the involved schemas, the mappings between the components of these schemas, and the database states which the schemas admit. Since satisfying some sort of schema equivalence property is essential in areas such as database conceptual design and database reverse engineering, our approach allows us to characterize the notion of normalization in database design. We review the current literature concerning database normal forms and decompositions. We also review the process of reverse engineering a database schema. In addition, we provide deeper insight into database reverse engineering methodologies, suggesting horizontal decompositions as a useful tool for facilitating the discovery of more specific objects and relationships in the conceptualization phase of the process. With the aid of simple examples, we show the essence behind our reasoning. We discuss the need for an unambiguous means through which objects in the output schema can be identified. Ultimately, the knowledge this paper ensues will be beneficial to database engineers in performing a correct schema transformation.

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the Datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator–wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management.

Temporal data exchange

Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Many applications such as planning, scheduling, medical and fraud detection systems, require data exchange in the context of temporal data. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time.In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls (which are introduced by data exchange and model incompleteness) in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naïve evaluation on the result of the c-chase and show it produces certain answers.

CDI: Configurable Data Integration Using Property Precedence Relations

Generally, data integration is performed through schema mapping representing high level correlation between heterogeneous data sources. Such mappings are generated using direct correspondences between data elements of source and target schemas, while other semantic relations are neglected. In this paper, we first use hierarchical relationships among properties (property precedence) as fundamental semantic relations within source and target schemas to semantically enhance schema mappings. Then, we use global property precedence relations between source and target elements to achieve Configurable Data Integration (CDI). This configurable setting allows trade-off between accuracy and completeness in query answering. Experiments using a working prototype of CDI show the potential of using this approach in various data integration scenarios.

DWSpyder: a new schema extraction method for a deep web integration system

The deep web is a huge part of the web that is not indexed by search engines. The deep web sources are accessible only through their associated access forms. We wish to use a web integration system to access the deep web sources and all of their information. To implement this web integration system, we need to know the schema description of each web source. The problem resolved in this paper is how to extract the schema describing an inaccessible deep web source. We propose our DWSpyder method as being able to extract the schema describing a deep web source despite its inaccessibility. The DWSpyder method starts with a static analysis of the deep web source access forms in order to extract the first elements of the associated schema description. The second step of our method is a dynamic analysis of these access forms using queries to enrich our schema description. Our DWSpyder method also uses a clustering algorithm to identify the possible values of deep web form fields with undefined sets of values. All of the information extracted is used by DWSpyder to generate automatically deep web source schema descriptions.

Approximate top-K answering under uncertain schema mappings

Data integration techniques provide a communication bridge between isolated sources and offer a platform for information exchange. When the schemas of heterogeneous data sources map to the centralized schema in a mediated data integration system or a source schema maps to a target schema in a peer-to-peer system, multiple schema mappings may exist due to the ambiguities in the attribute matching. The obscure schema mappings lead to the uncertainty in query answering, and frequently people are only interested in retrieving the best k answers (top-k) with the biggest probabilities. Retrieving the top-k answers efficiently has become a research issue. For uncertain queries, two semantics, by-table and by-tuple, have been developed to capture top-k answers based on the schema mapping probabilities. However, although the existing algorithms support certain features to capture the accurate top-k answers and avoid accessing all data from sources, they cannot effectively reduce the number of processed tuples in most cases. In this paper, new algorithms based on the histogram approximation and heuristic are proposed to efficiently identify the top-k answers for the data integration systems under uncertain schema mappings. In the experiments, the Histogram algorithm in the by-table semantics and the expected approach in the by-tuple semantics are shown to significantly reduce the number of processed tuples while maintaining high accuracy with the estimated probabilistic confidence.

DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

Rapid learning of sound schema for the cocktail party problem

Auditory scene analysis depends on knowledge of natural sound structure, but little is known about how source-specific structures might be learned and applied. We explored whether listeners internalize “schemas”—the abstract structure shared by different occurrences of the same type of sound source—during cocktail-party listening. We measured the ability to detect one of two concurrent “melodies” that did not differ in mean pitch (nor in timbre), ensuring that only the structure of these melodies over time could be used to distinguish them. Target melodies were cued by presenting them in isolation before each mixture, transposed to avoid exact repetition. The task was to determine if the cued melody was present in the subsequent mixture. Listeners performed above chance despite transposition between cue and target. Particular melodic schemas could recur across a subset of trials within a block, as well as across blocks separated by epochs in which the schema was absent. Recurrence across trials within a block facilitated target detection, and the advantage grew over the experiment despite intervening blocks, suggestive of learning. The results indicate that rapid and persistent internalization of source schemas can promote accurate perceptual organization of sound sources that recur intermittently in the auditory environment.

A Model for Multigranular Data and Its Integrity

Data involving spatial and/or temporal attributes are often represented at different levels of granularity in different source schemata. In this work, a model of such multigranular data is developed, which supports not only the usual order structure on granules, but also lattice-like join and disjointness operators for relating such granules in much more complex ways. In addition, a model for multigranular thematic attributes, to which aggregation operators are applied, is provided. Finally, the notion of a thematic multigranular comparison dependency, generalizing ordinary functional and order dependencies but specifically designed to model the kinds of functional and order dependencies which arise in the multigranular context, and in particular incorporating aggregation into the definition of the constraint, is developed.

Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity.

Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).

SEDEX: Scalable Entity Preserving Data Exchange

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. Generally, data exchnge is performed using schema mapping, representing high level relations between source and target schemas. In this paper, we argue that data exchange solely based on schema level information limits the ability to express semantics in data exchange. We show such schema level mappings not only may result in entity fragmentation, they are unable to resolve some ambiguous data exchange scenarios. To address this problem, we propose Scalable Entity Preserving Data Exchange (SEDEX), a hybrid method based on data and schema mapping that employs similarities between relation trees of source and target relations to find the best relations that can host source instances. Our experiments show SEDEX outperforms other methods in terms of quality and scalability of data exchange.