A method of expressing associated semanteme of entities

To make up for the defects of semanteme expression about linked data, this paper proposes a semanteme expressing method of associated entities based on relationship diagram so as to realize the machine expression and recognition of associated semanteme in relational databases. Starting with the structure and relationship of relational schema, this paper analyzes the rich semanteme of associated entities and presents the semanteme parsing method based on the traversal path as well as its formal expression; the analysis of instance database is also carried out. Studies show that this method can comprehensively parse and express the associated semanteme of entities. This work has reference significance for the research of intelligent semanteme synthesis and for semanteme-oriented intelligent query.

Schema Evolution in Multiversion Data Warehouses

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

BADF: Bat inspired Optimize solution for model transformation assisted with fitness function of Adaptive dragonfly algorithm (ADF)

Model transformation is the conspicuous research statement in the area of software engineering. Model transformation (MT) is playing the measure role in the Model driven engineering (MDE), which is helpful to transfer the model from one set of databases to another set of databases by considering the simulation and also support to various language. Propose work elaborate the Bat inspired optimize solution for model transformation using Adaptive Dragonfly Algorithm (BADF), and transform Class diagram (CLD) in to the relational schema (RS), accompanied by fitness function. Further performance of the proposed algorithm is appraised using Automatic Correctness (AC) and fitness measure, by comparing existing algorithm.

A reconstruction theory of relational schema induction

Learning transfer (i.e. accelerated learning over a series of structurally related learning tasks) differentiates species and age-groups, but the evolutionary and developmental implications of such differences are unclear. To this end, the relational schema induction paradigm employing tasks that share algebraic (group-like) structures was introduced to contrast stimulus-independent (relational) versus stimulus-dependent (associative) learning processes. However, a theory explaining this kind of relational learning transfer has not been forthcoming beyond a general appeal to some form of structure-mapping, as typically assumed in models of analogy. In this paper, we provide a theory of relational schema induction as a “reconstruction” process: the algebraic structure underlying transfer is reconstructed by comparing stimulus relations, learned within each task, for structural consistency across tasks—formally, the theory derives from a category theory version of Tannakian reconstruction. The theory also applies to non-human studies of relational concepts, thereby placing human and non-human transfer on common ground for sharper comparison and contrast. As the theory and paradigm do not depend on linguistic ability, we also have a way for pinpointing where aspects of human learning diverge from other species without begging the question of language.

Dynamic transformation of communal and exchange schemata in multicultural relationships

PurposeThe purpose of this paper is to conceptually distinguish between communal and exchange relationship schemata and analyze their dynamic interactions and transformations in multicultural contexts.Design/methodology/approachDrawing on theories of social capital, social exchange, networks and relational models, the authors propose a framework to conceptualize how the communal and exchange relationship schemata can be transformed, integrated and multiplied under contextual influences, especially in culturally complex settings faced by multinational organizations.FindingsThe authors elucidate the dynamic processes of schemata interactions and transformations in relationship management at interpersonal, interorganizational and national levels in a variety of intercultural contexts, including interactions between monoculturals from different cultures and interplay of cultures within biculturals and among multiculturals. The authors explain how schemata integration and fusion can provide competitive advantages in navigating multicultural relationships.Research limitations/implicationsSystematic qualitative and quantitative studies are recommended to further test and refine the proposed ideas regarding the dynamic interactions and transformations of relationship schemata.Practical implicationsThis paper presents implications for individuals, country managers and leaders who need to initiate and maintain relationships with culturally different others. The authors highlight the desirability of being aware of one's own relational schema, understanding others' schema, bridging the two schemata as well as fostering integration and fusion of the schemata.Social implicationsThe 2020 global pandemic and various social upheavals around the world highlight the urgency of finding effective mental models to manage relationships. The inclusive and adaptive ways of thinking about relationships can potentially facilitate harmonious connections and conflict resolution.Originality/valueThe authors conceptually disentangle two established relationship schemata and offer a model of their dynamic synergetic transformations.

A Temporal Multidimensional Model and OLAP Operators

Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.

Effects of "chained" study on spontaneous relational discovery

Prior knowledge of relational structure allows people to quickly make sense of and respond to new experiences. When awareness of such structure is not necessary to support learning, however, it is unclear when and why individuals “spontaneously discover” an underlying relational schema. The present study examines the determinants of such discovery in discrimination-based transitive inference (TI), whereby people learn about a hierarchy of interrelated premises and are tested on their ability to draw inferences that bridge studied associations. Experiencing “chained” sequences of overlapping premises during training was predicted to facilitate the discovery of relational structure. Among individuals without prior knowledge of the hierarchy, chaining improved relational learning and was most likely to result in explicit awareness of the underlying relations between items. These findings add to growing evidence that the temporal dynamics of training, including successive presentation of overlapping associations, are key to understanding spontaneous relational discovery during learning.