This paper is aimed to overcome the limitation of the traditional multidimensionalmodel in order to allow usage of numerical, textual and object-oriented information as multidimensionalmodel measures. The ontologica...
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ISBN:
(数字)9783319743134
ISBN:
(纸本)9783319743134;9783319743127
This paper is aimed to overcome the limitation of the traditional multidimensionalmodel in order to allow usage of numerical, textual and object-oriented information as multidimensionalmodel measures. The ontological approach is reviewed. The formal definition of multidimensional approach is given. The idea of multidimensional and ontological approaches hybridization is discussed. The hybrid multidimensional-ontological datamodel requirements are proposed. The formal definitions of the metagraph datamodel and metagraph agent model are given. The examples of data metagraph and metagraph rule agent are discussed. The representation of object-oriented data structures in form of metagraph is given. The hybrid multidimensional-ontological datamodel based on metagraph approach is proposed. Predicate representation of metagraph model considered as a physical datamodel for metagraph approach implementation is given.
data structure and semantics of the traditional datamodel cannot effectively represent the data warehouse, it is difficult to effectively support online analytical processing （referred to as OLAP）. This paper is pr...
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data structure and semantics of the traditional datamodel cannot effectively represent the data warehouse, it is difficult to effectively support online analytical processing （referred to as OLAP）. This paper is propose a new multidimensional data model based on the partial ordering and mapping. The datamodel can fully express the complex data structure and semantics of data warehouse, and provide an OLAP operation as the core of the operation of algebra, support structure in levels of complex aggregation operation sequence, which can effectively support the application of OLAE The datamodel supports the concept of aggregation function constraint, and provides constraint mechanism of the hierarchy aggregation function.
This research proposes an adoption of data warehousing concepts to create a movie recommender system. The data warehouse is generated using ETL process in a desired star schema. The profiles of users and movies are cr...
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ISBN:
(纸本)9783662452370;9783662452363
This research proposes an adoption of data warehousing concepts to create a movie recommender system. The data warehouse is generated using ETL process in a desired star schema. The profiles of users and movies are created using multidimensional data model. The data are analyzed using OLAP, and the reports are generated using data mining and analysis tools. The recommended movies are selected using multi-criteria candidate selection. The movies which present the genres that match individual preference are recommended to the particular user. The multidimensional data model and OLAP provide high performance to discover the new knowledge in the big data.
data structure and semantics of the traditional datamodel cannot effectively represent the data warehouse, it is difficult to effectively support online analytic processing(referred to as OLAP). This paper is propose...
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data structure and semantics of the traditional datamodel cannot effectively represent the data warehouse, it is difficult to effectively support online analytic processing(referred to as OLAP). This paper is propose a new multidimensional data model based on the partial ordering and mapping. The datamodel can fully express the complex data structure and semantics of data warehouse, and provide an OLAP operation as the core of the operation of algebra, support structure in levels of complex aggregation operation sequence, which can effectively support the application of OLAP. The datamodel supports the concept of aggregation function constraint, and provides constraint mechanism of the hierarchy aggregation function.
With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opin...
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With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opinions, called Opinion Aggregation, which is made up of four steps: Collect, Identify, Classify and Aggregate. In this paper, we present a new conceptual multidimensional data model based on the Fuzzy model based on the Semantic Translation to solve the Aggregate step of an Opinion Aggregation architecture, which allows exploiting the measure values resulting from integrating heterogeneous information (including unstructured data such as free texts) by means of traditional Business Intelligence tools. We also present an entire Opinion Aggregation architecture that includes the Aggregate step and solves the rest of steps (Collect, Identify and Classify) by means an Extraction, Transformation and Loading process. This architecture has been implemented in an Oracle Relational database Management System. We have applied it to integrate heterogeneous data extracted from certain high end hotels websites, and we show a case study using the collected data during several years in the websites of high end hotels located in Granada (Spain). With this integrated information, the data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability and a high precision by means of interactive tools such as the dashboards.
We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and Business In...
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We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and Business Intelligence (BI) tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table, which aggregation functions correctly aggregate the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes that are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules that transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.
Precision agriculture harnesses advanced technology and data analytics to enhance the efficiency and cost-effectiveness of agricultural production. This paper presents a comprehensive case study focused on optimizing ...
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Integrated logistics support (ILS) is of great significance for maintaining equipment operational capability in the whole lifecycle. Numerous segments and complex product objects exist in the process of equipment ILS,...
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Integrated logistics support (ILS) is of great significance for maintaining equipment operational capability in the whole lifecycle. Numerous segments and complex product objects exist in the process of equipment ILS, which gives ILS data multi-source, heterogeneous, and multidimensional characteristics. The present ILS data cannot satisfy the demand for efficient utilization. Therefore, the unified modeling of ILS data is extremely urgent and significant. In this paper, a unified datamodeling method is proposed to solve the consistent and comprehensive expression problem of ILS data. Firstly, a four-tier unified datamodeling framework is constructed based on the analysis of ILS data characteristics. Secondly, the Core unified datamodel, Domain unified datamodel, and Instantiated unified datamodel are built successively. Then, the expressions of ILS data in the three dimensions of time, product, and activity are analyzed. Thirdly, the Lifecycle ILS unified datamodel is constructed, and the multidimensional information retrieval methods are discussed. Based on these, different systems in the equipment ILS process can share a set of datamodels and provide ILS designers with relevant data through different views. Finally, the practical ILS datamodels are constructed based on the developed unified datamodeling software prototype, which verifies the feasibility of the proposed method.
The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It ap...
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High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this paper, we ...
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High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this paper, we study the problem of inconsistency detection and repair of the Ontology Multi-dimensional datamodel (OMD). We propose a framework of data quality assessment, and repair for the OMD. We formally define a weight-based repair-by-deletion semantics, and present an automatic weight generation mechanism that considers multiple input criteria. Our methods are rooted in multi-criteria decision making that consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain. (C) 2019 The Authors. Published by Elsevier B.V.
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