Abstract: Data Science, considered as a science by itself, is in general terms, the extraction of knowledge from data. Symbolic data analysis SDA gives a new way of thinking in Data Science by extending the standard input to a set of classes of individual entities. Hence, classes of a given population are considered to be units of a higher level population to be studied. Such classes often represent the real units of interest. In order to take variability between the members of each class into account, classes are described by intervals, distributions, set of categories or numbers sometimes weighted and the like. In that way, we obtain new kinds of data, called 'symbolic' as they cannot be reduced to numbers without losing much information. The first step in SDA is to build the symbolic data table where the rows are classes and the variables can take symbolic values. The second step is to study and extract new knowledge from these new kinds of data by at least an extension of Computer Statistics and Data Mining to symbolic data. SDA is a new paradigm which opens up a vast domain of research and applications by giving complementary results to classical methods applied to standard data. SDA also gives answers to big data and complex data challenges as big data can be reduced and summarized by classes and as complex data with multiple unstructured data tables and unpaired variables can be transformed into a structured data table with paired symbolic-valued variables. WIREs Comput Stat 2016, 8:172-205. doi: 10.1002/wics.1384

Abstract: For more than a decade, OLAP and multidimensional analysis have generated methodologies, tools and resource management systems for the analysis of numeric data. With the growing availability of semistructured data there is a need for incorporating text-rich document data in a data warehouse and providing adapted multidimensional analysis. This paper presents a new aggregation function for keywords allowing the aggregation of textual data in OLAP environments as traditional arithmetic functions would do on numeric data. The AVG_KW function uses an ontology to join keywords into a more common keyword.

Abstract: We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Renyi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data with the examples of several images and discuss further possible utilizations in other fields of data processing.

Abstract: Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

Abstract: The invention discloses an on-line analyzing and processing system of social security data on the basis of a distributed data warehouse. The on-line analyzing and processing system comprises a display layer, a dimensional layer, a star-shaped layer and a storage layer, wherein the display layers interacts with a multidimensional analysis server; the dimensional layer analyzes a MDX (Multidimensional Expressions) statement and executes query computation by the analyzed statement; the star-shaped layer manages the cache of an aggregation result; and the storage layer is in charge of receiving a SQL (Structured Query Language) statement emitted from the start-shaped layer, executing the SQL statement in the data warehouse and returning a result. The data warehouse of the system is realized through a distributed file system, and a distributed file system cluster is uniformly managed via a cloud computation platform to realize the load balancing of resources. By use of the system, through functional multilayer distribution, a uniform query interface for the distributed data warehouse which stores mass data is realized, and a multidimensional operation for a data cube is finished by a query way similar to JDBC (Java DataBase Connectivity).

Abstract: The shift to more parallel and distributed computer architectures changed how data is managed consequently giving birth to a new generation of software products, namely NoSQL. These products offer a scalable and reliable solution for "Big Data", but none of them solves the problem of analyzing and visualizing multidimensional data. There are solutions for scaling analytic workloads, for creating distributed databases and for indexing multidimensional data, but there is no single solution that points to all three goals together.We propose the D8-tree, a De-normalized Octa-tree index that supports all three goals. It works with both analytical and data-thinning queries on multidimensional data ensuring, at the same time, low latency and a linear scalability. We have implemented a D8-tree prototype, and we compared it with PostGIS on a set of queries required by an in-house HPC application. We found the D8-tree outperforming PostGIS in all tested queries, with a performance gain up to 47 times.

Abstract: The present invention provides a Web-based multidimensional analysis system and method. The system comprises a data set definition tool used for defining a target data source; a Web pivot table management tool used for obtaining a target multidimensional analysis scheme; a Web pivot table tool used for arranging row dimension fields, column dimension fields, statistics fields and graph types specified to display; a data source obtaining engine used for obtaining target data; an interface data calculating engine used for performing multidimensional statistics on the obtained target data and obtaining multidimensional statistics results; and a graph data display interface used for displaying the multidimensional statistics results. According to the scheme, multidimensional analysis of the data corresponding to the data source can be achieved, the multidimensional analysis system can be maintained only from a server terminal for later maintenance, and complexity of the later maintenance is reduced.

Abstract: Data Intensive Computing for Scientific Research needs effective tools for data capture, curate them for designing appropriate algorithms and multidimensional analysis for effective decision making for the society. Different computational environments used for different data intensive problems such as Sentiment Analysis and Opinion Mining of Social media, Massive Open Online Courses (MOOCs), Large Hadron Collider of CERN, Square Kilometer Array (SKA) of radio telescopes project, are usually capable of generating exabytes (EB) of data per day, but present situations limits them to more manageable data collection rates. Different disciplines and data generation rates of different lab experiments, online as well as offline, make the issue of creating effective tools a formidable problem. In this paper we will discuss about different data intensive computing tools, trends of different emerging technologies, how big data processing heavily relying on those effective tools and how it helps in creating different models and decision making.

Abstract: Multidimensional databases are well-suited for viewing data at different levels of detail. Graph databases are well-suited for modeling data sets with complex relationships. A novel platform for analysis and planning is enabled by linking multidimensional and graph databases. Graphs are data structures stored in graph databases. Graphs use nodes and edges to model data elements, some of which are derived. A graph is traversed to derive new data elements. To perform analysis on the graph data elements, graph traversal paths are stored as tuples in a fact table. This fact table is in turn loaded into the multidimensional database by mapping the fact table's attribute columns to dimensions of the multidimensional database.

Abstract: This paper presents the performance evaluation of MRDataCube which we have previously proposed as an efficient algorithm for data cube computation with data reduction using MapReduce framework. We performed a large number of analyses and experiments to evaluate the MRDataCube algorithm in the MapReduce framework. In this paper, we compared it to simple MR-based data cube computation algorithms, e.g., MRNaive, MR2D as well as algorithms converted into MR paradigms from conventional ROLAP (relational OLAP) data cube algorithms, e.g., MRGBLP and MRPipeSort. From the experimental results, we observe that the MRDataCube algorithm outperforms the other algorithms in comparison tests by increasing the number of tuples and/or dimensions.

Abstract: Similarity join is arguably one of the most important operators in multidimensional data analysis tasks. However, processing a similarity join is costly especially for large volume and high dimensional data. In this work, we attempt to process the similarity join on MapReduce such that the join computation can be scaled horizontally. In order to make the workload balancing among all MapReduce nodes, we systemically select the most profitable feature based on a novel data selectivity approach. Given the selected feature, we develop the partitioning scheme for MapReduce processing based on two different optimization goals. Our proposed techniques are extensively evaluated on real datasets.

Abstract: The invention discloses a GPU cluster-based multidimensional big data factorization method, aims to solve the problem that a conventional grid parallel factor analysis model cannot process large-scale and high-dimension multidimensional data analysis, and provides an effective pattern processing unit-based multidimensional big data multi-mode decomposition method, namely a hierarchical parallel factor analysis framework. The framework is based on the conventional grid parallel factor analysis model, comprises a process of integrating tensor subsets under a coarse-grained model and a process of calculating all of the tensor subsets and fusing factor subsets under a fine-grained model, and is operated on a cluster formed by a plurality of nodes; each node comprises a plurality of pattern processing units. Tensor decomposition on pattern processing unit equipment can fully utilize a powerful parallel computing capability and a paralleling resource generated in tensor decomposition; experimental results show that through the adoption of the method, executive time for acquiring tensor factors can be greatly shortened, the large-scale data processing capability is improved, and the problem that the computing resource is insufficient is well solved.

Abstract: This paper presents the information system of multidimensional data analysis and data mining by identification of associative dependences in multidimensional data, which was implemented in post-relational DBMS Cache Environment. Information system modules have been considered, which perform the next tasks: design of object database on the physical level and its provisioning, construction of multidimensional data structures for creation a database and association rules mining among multidimensional data. Methods of OLAP cubes construction have been considered as well as association rules mining in them which were implemented in the information system.

Abstract: Multidimensional data analysis is considerably important when dealing with such large and complex datasets. Among the possibilities when analyzing such kind of data, applying visualization techniques can help the user find and understand patters, trends and establish new goals. This thesis aims to present several visualization methods to interactively explore multidimensional datasets aimed from specialized to casual users, by making use of both static and dynamic representations created by multidimensional projections.

Abstract: Spatial data warehouse refers to data warehouse which has a spatial component that represents the geographic location of the position or an object on the Earth's surface. Spatial data warehouse can be visualized in the form of a crosstab tables, graphs, and maps. Spatial data warehouse of hotspot in Indonesia has been constructed by researchers from FIRM NASA 2006-2015. This research develops multidimensional analysis module and location intelligence module using SpagoBI. The multidimensional analysis module is able to visualize online analytical processing (OLAP). The location intelligence module creates dynamic map visualization in map zone and map point. Map zone can display the different colors based on the number of hotspot in each region and map point can display different sizes of the point to represent the number of hotspots in each region. This research is expected to facilitate users in the presentation of hotspot data as needed.

Abstract: The aim of this paper is to present a model of capability measurement, called MACaD (Multidimensional Analysis of Capabilities Deprivation), which is based on the observation of a list of functionings in a sample of people through an empirical analysis. Within the theoretical framework of the capability approach developed by Amartya K. Sen, we introduce an analytical definition of the concept of functioning that encompasses individual agency. Furthermore, we develop a multidimensional index based on the counting approach which allows us to represent individuals within a Cartesian space. Empirical analysis is carried out through a specific questionnaire administered to more than 500 individuals living in the Municipality of Rome 10. We have focused our attention on specific subgroups of our sample (households with children), a peculiar life dimension (expressing emotions) and capabilities transformation deficit.

Abstract: The raster and vector spatial data models are the most commonly used in geographic information systems (GIS) practice but are insufficient for the representation of dynamic spatiotemporal phenomena that operates in multiple dimensions. Although numerous improvements to the spatial data models have been proposed and various prototype implementations have been developed in order to address this limitation, the problem has persisted. One of the proposals for spatial data representation is the geoatom data model which is a theoretical concept used for conceptualizing geographic information as it pertains to the four-dimensional (4D) space-time continuum. The objective of this study is to develop and apply the spatiotemporal queries in order to analyze and explore the evolution of the 4D geospatial process. The geo-atom data model and voxel automata have been used for the simulation of a dynamic 4D process of snow cover retreat in order to test the developed theoretical concepts. The developed queries for spatio-temporal analysis are volume, surface area, rate of change and temporal ordering. The data used have spatial, temporal and attribute components and represent the voxels units generated from the simulation of the process. Obtained results are the outcomes of various spatio-temporal queries that permit the analysis of the snow retreat process in 4D. This study contributes to conceptual and applied advancements in the field of 4D GIS and multidimensional analysis, and is relevant to geography, earth and environmental sciences, the disciplines where the phenomena need to be studied in 4D.

Abstract: The invention provides a technical solution for a hand-held mobile geographic information terminal system capable of monitoring economic operation data. The hand-held mobile geographic information terminal system comprises an economic index data analysis display module, a key monitoring module, a comprehensive query module, a supervision service module, a decision-making analysis module, a data acquisition module, a data communication module and a path analysis and map data buffer memory module. The system can collect, store, manage, operate, analyze, display and describe main market player information and relevant economic data in a whole or partial regional space; data of all departments are linked to a spatial geographic information system, comprehensive information of main market players is displayed at spatial positions; scientific, accurate and timely forecasting and early warning can be realized; a regional economic operation data condition can be timely and comprehensively grasped in a visual way; multidimensional analysis, early warning and decision-making support service can be provided; data collection, photograph shooting and video recording can be conducted on site, and information collection and relevant work can be carried out conveniently.

Abstract: We are midst of digital inclusion era, where different technologies around us, providing a wide platform of engagement with meaningful data visualizations. That engagement demands rational sense from end-users’ to handle data in more sensitive manner. Large datasets for research need effective tools for data capture, curate them for designing appropriate algorithms and multidimensional analysis for effective visualizations. Effective use of ICT will help us a lot to curve with societal problems in multidimensional way. While exploring different activities around an event, selecting trusted sources using different visualization cum analysis tools are a handy option for journalists, government and common people. The complexity and volume of the data produced by an event remains largely untapped. Exploratory Data Analysis (EDA) with proper visualization techniques helped us a lot to demonstrate our ability to build an environment for heterogeneous large volume datasets. Different disciplines and data generation rates of different lab experiments, online as well as offline make the issue of creating effective visualization tools a formidable problem. Our main aim is to analyze and summarize large datasets in a concise manner with or without help of any statistical tools and produce the results using different visualization techniques In this paper we will discuss about different data intensive visualization tools, trends of different emerging technologies, how big data processing heavily relying on those effective tools and how it helps in efficient decision making for the society.

Abstract: Direct modeling and simulation of engineering problems with various irregularities are computationally very inefficient and in some cases impossible, even in these days of massively parallel computational systems. As a result, in recent times, a number of schemes have been put forward to tract such problems in a computationally efficient manner. Needless to say, such schemes are still going through evolutionary stages. This paper addresses direct solution based on the selective use of different dimensional models at different regions of the problem domain. For the multidimensional approach, a higher-order transition element is developed to connect the different element types where two- and three-dimensional laminated elements based on higher-order subparametric concept are considered. Modeling simplicity and calculation efficiency of the multidimensional approach are shown for the analysis of cantilever plates with stepped section and patch-repaired plates.

Abstract: In order to improve the operational efficiency of decision-making in the context of spacecraft (SC) endurance in operation, the task was set to increase the efficiency of adaptation of its control system to the environmental effects. The instruments installed on most Russian SCs in many cases do not provide for identification and timely elimination of accident sources due to delays in the identification of faults and failures. A technology is proposed that involves intellectualization of control systems. It is suggested to complement the SC control circuit with an expert system that includes a “prognostic decision support system”, a “control simulation and correction module”. Due to the ambiguity and common uncertainty of cosmic phenomena, it is suggested to predict the reaction of SC equipment to external effects rather than monitor such effects. The intelligence of the expert system is to be ensured through the analysis of the communication medium that defines the possibility to ensure SC survivability. The correction of control is suggested to be performed not on the basis of process parameters monitoring, but rather knowledge. This knowledge is held by experts who possess experience in SC flight mission performance. The results of the audit of external factors and development of SC functional units reactions represent the input data. After clearing, sorting and statistical analysis of data, it is suggested to regard it as information resources. The results of such resources analysis and design of messages based on expert conclusions transforms such resources into knowledge that is used in decision making and control correction. The diversity of architectures and processes of SC functional units design has defined the requirement to involve experts with diverse professional backgrounds. It was proposed to generate forecasts of SC equipment reactions development in the form of description of the dynamics of multifactor combination of the results of intersubject audit of functional units operations and subjective expert evaluations. In order to ensure agility of information analysis within the knowledge base, it was suggested to use the OLAP comprehensive multidimensional analysis technology. In particular, that regards fast analysis of shared multidimensional information that includes requirements for multidimensional analysis applications. The proposed model of systematic accumulation and processing of knowledge will enable flight control officers to timely identify inadequacies in the control inputs. The logical and statistical analysis capabilities ensured by this application will enable the delivery of analysis results to the experts within a time period sufficient for elimination of the causes of faults and failures in SC equipment operation. Multidimensional conceptual representation of data including the support of multiple hierarchies will define the capability to refer to any required information regardless of its size and place of storage. The proposed method of information support of SC equipment reactions forecasting is addressed in the light of the analysis of electrophysical effects that affect SC in near-Earth orbits. Combining the methods of computer data processing and intersubject analysis of functional units operation must insure efficient decision-making based on increased accuracy and agility of data processing and, consequently, selection of SC operation adaptation scenario subject to flight control officer’s preferences.

Abstract: The present invention discloses a method for constructing a unified data model for an enterprise. The method comprises the following steps: first, using a collection layer to collect data from a data source regularly or in real time; storing the collected data on a storage layer of a data center together, and performing classified storage according to different storage platforms; after the classified storage is completed, a calculation layer calculating the stored data to obtain a calculation result; an analysis layer performing targeted analysis on the calculation result, wherein the targeted analysis comprises statistical analysis, warning analysis, prediction analysis, multidimensional analysis, data mining, real-time decision-making and a report query capability; and a display layer displaying an analyzed analysis structure on a PC terminal, a large-screen terminal or a mobile terminal in the form of a text, or a number, or a graph. According to the method, the architecture of the data center is optimized, and unified processing is performed on the data, so as to greatly improve the service quality and service efficiency of digitization of the power system.

Abstract: The invention provides a user data prediction method and prediction device. The method comprises: performing data hierarchy on historic user data according to a reserved hierarchy rule to obtain hierarchical user data; calculating the transition probability of user data among hierarchies; utilizing a reserved training model to train the transition probability so as to determine the transition matrix of user data among hierarchies; and performing forecast calculation on current user data through the transition matrix so as to determine prediction user data. The method can perform data analysis and modeling on the transition probability of user data among hierarchies so as to determine the transition matrix of user data among hierarchies; the transition matrix obtained by training user transition states among hierarchies realizes user data multidimensional analysis, and provides a reliable guarantee for prediction result accuracy; meanwhile, accurate prediction user data provides a sound data reference basis for product technical tuning and market decision.

Abstract: FIELD: computer engineering.SUBSTANCE: invention can be used in analysis and simulation of hardly formalised processes characterised by a large number of considered factors, which requires use of special methods and instruments for multidimensional analysis of different-quality information. Method of automatic clustering of objects includes formation from the initial set of classified objects of samples in the form of initial clusters; at that, the initial set is formed by identifying every object on the basis of its parameter setting the coordinate of the object in the initial set, and is considered as a training sample formed on the basis of exponential law of distribution, while data on clusters obtained at the stage of training, is registered on corresponding elements of a memory used later during successive accumulation in them of measurement information; at the stage of training, there is also determined the model of cluster Kwith the number of elements N, meeting the requirement for minimum risk R(α) when forming a cluster model.EFFECT: technical result consists in improvement of serial clustering stability.1 cl, 2 dwg

Abstract: Today, all important educational achievement studies, particularly large scale assessments, use item response theory (IRT) as the standard method for their analyses An important and very basic assumption of IRT is on the given dimensionality of a test: In order to be interpreted unidimensional a test has to be unidimensional and hence cannot be interpreted multidimensional In reality though, this basic assumption is very often neglected The Programme for International Student Assessment (PISA), for example, applies a unidimensional IRT-Model for the analysis of the mathematics achievement and at the same time applies a multidimensional IRT-model for the analysis of the subscales Change and Relationships, Quantity, Space and Shape, and Uncertainty and Data This contradiction to one of the basic assumptions of IRT is also included in other well known large scale assessments Strangely enough, non of these studies discuss the issue This work discusses the advantage and disadvantages of the currently used approaches for a concurrent uni- and multidimensional analysis, and a new approach is presented, which bases on an IRT model, the generalized subdimension model (GSM), which allows the calculation of a weighted mean score within the IRT framework Besides the demonstration of different applications for the GSM and its predecessor, the subdimension model, the model’s characteristics are compared to those of other models, such as hierarchical models Beyond the comparison of the model fit, that is, the reliability of the results, the discussion particularly focuses on the difference in the interpretation, that is, on the validity of the results