Abstract: Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.

Abstract: We review results concerning the representation of partial orders of univariate distributions via stochastic orders and investigate their applications to some classes of stochastic dominance conditions applied in inequality and welfare measurement. The results obtained in an unidimensional framework are extended to multidimensional analysis. We discuss difficulties arising from aggregation of multidimensional distributions into synthetic indicators that value both inequality in the distribution of each attribute and the association between the attributes. We explore the potential for multidimensional evaluations that are based on the partial orders induced by different criteria of majorization and organize related and equivalent inequality and welfare representations.

Abstract: Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

Abstract: This Chapter considers approaches to video skimming into semantically-consistent segments of video streams, which are highly redundant and weakly structured data. In such a way, one of the promising ways is spatial-temporal segmentation as frame partitions represent certain spatial image content. Also, properties were formulated and proved which ultimately determine the characteristics of permissible segmentation transformations when searching for a compromise between over and undo segmentation. Temporal segmentation of multidimensional time series has been examined, which enables structuring video streams and significantly reduce the amount of data that will require online processing. For this, multidimensional time series analysis theory was used, since a completely natural video representation is a sequence of frames, followed by their combination into groups of frames (shots) with the same content. It was shown that various approaches can be used to detect shots with homogeneous characteristics, which are based on VAR models, exponential smoothing and predictive models.

Abstract: Nowadays, a great deal of attention is devoted to the relevant problem of supporting big data analytics from social systems (e.g., social networks, smart city applications, skill management platforms, and so forth). Following this innovative trend, the opportunity of adopting advanced OLAP-based tools for supporting the knowledge extraction phase from big social data represents the new frontiers for big social data computing. Indeed, the well-known features of multidimensional data analysis are able to support a "rich" extraction of actionable knowledge, beyond actual limitations of alternative procedural approaches. In line with this emerging research challenge, this paper explores benefits, limitations and challenges of OLAP-based big data analytics tools over (big) social data.

Abstract: Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 11 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. All our results are documented and made available in a public repository to allow reproducibility of results.

Abstract: As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the Internet of Things systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multidimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real world scenarios.

Abstract: —Theoretical and applied aspects of using analytical post-processing methods using multidimensional data analysis to protect resources in shared systems are investigated. New approaches, algorithms, and procedures of this process are considered on the basis of registration statistics and multidimensional data analysis, which allow counteracting the implementation of implicit indirect methods of unauthorized access (or other actions) to information. A method of evaluating the quality of monitored indicators, as well as the stationary state of the system of indicators that characterize the “image” of the user in the system, is proposed. Questions of perception of results of data post-processing by a decision maker (security service administrator) are analyzed. A method of graphical visualization of results of registration and analytical processing of fixed data is presented.

Abstract: In highway domain, business analyses are always multidimensional on massive data for the traffic monitor and control. It is tedious to develop data analysis jobs from scratch and is hardly to consider comprehensive factors from requirements. In this paper, we propose a domain specific service template on massive toll data in highway domain. Based on the service template, abundant multidimensional analysis jobs as services can be built and managed flexibly. In a practical project, our method proves the feasibility and advantages by exhaustive experiments and case studies.

Abstract: The article deals with the issue of conceptual modelling of the MOLAP database structures used in multidimensional data analysis for BI. Management information for decision-making purposes was collected in the MOLAP database and delivered to the user via the management cockpit. Building OLAP databases is discussed from the database designer point of view The meta model of the OLAP database was described in conceptual and logical terms. The work includes a critical analysis of the meta-model assumptions for the design of systems for multidimensional data analysis. A postulate to revise the classic meta-model was presented and a new type of relationship was proposed. The assumptions of the meta-model for modelling and simulating business decisions were presented, as well as the application of meta modeling to define business processes. Based on Gozinto graphs, the meta model of structural and technological developments was defined

Abstract: Disclosed is a design expert system comprising: a predictive model generation unit for generating a predictive model by using experimental data collected by a design target experiment and keywords describing the experimental data; a data generation unit for generating characteristic data using an approximate model for identifying characteristics of a design object; a data analysis unit for analyzing the generated characteristic data; and a design optimization unit for performing design optimization of the design target according to the optimal design algorithm using various data analysis results based on the prediction model According to the present invention, it is possible to obtain an optimized performance improvement result only by inputting data in an optimal design and multidimensional data analysis and setting a design target

Abstract: The ATLAS experiment at the Large Hadron Collider has a complex heterogeneous distributed computing infrastructure, which is used to process and analyse exabytes of data. Metadata are collected and stored at all stages of data processing and physics analysis. All metadata could be divided into operational metadata to be used for the quasi on-line monitoring, and archival to study the behaviour of corresponding systems over a given period of time (i.e. long-term data analysis). Ensuring the stability and efficiency of complex and large-scale systems, such as those in the ATLAS Computing, requires sophisticated monitoring tools, and the long-term monitoring data analysis becomes as important as the monitoring itself. Archival metadata, which contains a lot of metrics (hardware and software environment descriptions, network states, application parameters, errors) accumulated for more than a decade, can be successfully processed by various machine learning (ML) algorithms for classification, clustering and dimensionality reduction. However, the ML data analysis, despite the massive use, is not without shortcomings: the underlying algorithms are usually treated as "black boxes", as there are no effective techniques for understanding their internal mechanisms. As a result, the data analysis suffers from the lack of human supervision. Moreover, sometimes the conclusions made by algorithms may not be making sense with regard to the real data model. In this work we will demonstrate how the interactive data visualization can be applied to extend the routine ML data analysis methods. Visualization allows an active use of human spatial thinking to identify new tendencies and patterns found in the collected data, avoiding the necessity of struggling with the instrumental analytics tools. The architecture and the corresponding prototype of Interactive Visual Explorer (InVEx) - visual analytics toolkit for the multidimensional data analysis of ATLAS computing metadata will be presented. The web-application part of the prototype provides an interactive visual clusterization of ATLAS computing jobs, search for computing jobs non-trivial behaviour and its possible reasons.

Abstract: Based on the characteristics of clinical medical literature keywords including syndrome, disease and treatment, a multidimensional analysis system for medical literature retrieval keywords is designed to satisfy the needs of users. The system focuses on the analysis of medical literature retrieval keywords, including document information management, word frequency analysis, multidimensional statistical analysis and other functional modules, which can effectively solve the problems of batch retrieval of imported documents, data standardization, multidimensional statistical analysis according to the keyword attribute. Taking the Chinese medicine treatment of psoriasis as an example, the use and application of the system and multidimensional statistical analysis will be introduced. The system is completed to make a quick, convenient and accurate multidimensional analysis of the keywords with the aid of computers, and provides reference for the design and improvement of other medical data analysis systems.

Abstract: In order to assist a certain automobile sales company in scientific decision-making in management, this paper builds a data warehouse through demand analysis, source data preparation, modeling, extraction and other steps, and carries out multidimensional analysis, report visualization and other applications. The results show that the data warehouse can effectively support the company’s sales analysis and decision-making, and also provide some experience for the implementation of similar projects in other enterprises.

Abstract: This paper outlines the recommendation of analytical tools likely to be derived from the data recorded within the industrial automation system. The means might facilitate optimization of process efficiency, especially in terms of energy efficiency. Basically, each electromechanical device is electrically charged and controlled by the industrial automation system. A kind of the signal usually depends on various operational modes of the given device which are classified by its load. Available signal segmentation and statistical methods lead to the automatic identification of these modes and working patterns or abnormal performances caused by poor technical condition. Therefore, simple electrical signal allows to count the real device performance time and utilities usage, to identify its working modes, to recognize process losses, to specify KPI factors and to develop diagnostics. This paper describes multidimensional processing of conveyor stream data along with their exemplary use in real-time data. The algorithm of identifying operational regimes is characterized based on machine learning and further in-context analyses paired with visualisations.

Abstract: In recent researches, ABC-analysis of the assortment is presented in the format, when the assortment positions are divided into groups by only one parameter – income. The purpose of the article is to develop an algorithm for conducting a multidimensional ABC analysis of the range and options for interpreting its results. The conditional example in the article proposes a multidimensional analysis format that covers three blocks – one-dimensional analysis by revenue, two-dimensional by income and profit, and then three-dimensional – by income, profit and quantity of sales. Based on the results of each block, a model of interpretation of the obtained results is proposed, it includes recommendations for optimization of the range. Product grouping is presented in matrix and table forms, which allows to better visualize product line items and justify product group priorities.

Abstract: Pivot tables are one of the most popular tools for data visualization in both business and research applications. Although they are in general easy to use, their comprehensibility becomes progressively lower when the quantity of cells to be visualized increases (i.e., information flooding problem). Pivot tables are largely adopted in OLAP, the main approach to multidimensional data analysis. To cope with the information flooding problem in OLAP, the shrink operation enables users to balance the size of query results with their approximation, exploiting the presence of multidimensional hierarchies. The only implementation of the shrink operator proposed in the literature is based on a greedy heuristic that, in many cases, is far from reaching a desired level of effectiveness. In this paper we propose a model for optimizing the implementation of the shrink operation which considers two possible problem types. The first type minimizes the loss of precision ensuring that the resulting data do not exceed the maximum allowed size. The second one minimizes the size of the resulting data ensuring that the loss of precision does not exceed a given maximum value. We model both problems as set partitioning problems with a side constraint. To solve the models we propose a dual ascent procedure based on a Lagrangian pricing approach, a Lagrangian heuristic, and an exact method. Experimental results show the effectiveness of the proposed approaches, that is compared with both the original greedy heuristic and a commercial general-purpose MIP solver.

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