RePivot - Developing Analysis Services with Column Store Database

This paper proposes a novel time series data mining and analysis framework inspired by ancient Chinese culture I-Ching. The proposed method converts the time series into symbol spaces by employing the concepts and principles of I-Ching. Algorithms are addressed to explore and identify temporal patterns in the resulting symbol spaces. Using the analysis framework, major topics of time series data mining regarding time series clustering, association rules of temporal patterns, and transition of hidden Markov process can be analyzed. Dynamic patterns are derived and adopted to investigate the occurrence of special events existing in the time series. A case study is illustrated to demonstrate the effectiveness and usefulness of the proposed analysis framework. Key words: Time series, data mining, Chinese I-Ching.

Description of the problem: Despite long-term commitment to decentralization in India, progress towards a decentralized form of governance has been slow. In Karnataka, despite state compliance with decentralised governance legislation from the mid-1980s, local bodies continue to suffer from inadequate power and resources. Under the National Rural Health Mission (NRHM), decentralization plays a role in monitoring service access and quality. However, after almost a decade of organized decentralisation in the health sector, its impact is still not clear, nor its ability to enhance individuals/communities participation and ability to fulfil their health needs. Operational Definition : For the purpose of this research, we define decentralisation as a process which involves shifting of power and responsibilities between tiers of government by way of various fiscal, political, and administrative instruments. Rationale: Regardless of these arguments for and against decentralization, there is little evidence that shows the effects of decentralisation on health service delivery and changes in health indicators. There are few studies that have tried to assess the impact of decentralization on the provision of health care services and health outcomes and there is a lack of an analytical framework to empirically analyse its impact. As a result, there is limited empirical evidence on the impact of decentralization on improving delivery of health care services and health outcomes worldwide. Research Questions: The main questions we analyse through this study are: Does the degree of decentralisation under the NRHM correlate strongly with perceived decision space of Health Department officials and PRI members at the District level and below? Does the capacity of functionaries at different levels of the health system correlate with perceived decision space? Does greater perceived decision space by any given functionary/official lead to better health outcomes? Conceptual Framework : For the research purpose, we use Bossert’s analytical frame work. This framework outlines the concept of “Decision Space”, by studying the range of choice that different actors in the health system perceive as being available to them along a series of functional dimensions. Decentralization is expected to enhance service delivery of public services, as well as have a positive impact on both the performance and health outcomes in various country settings. A successful model of decentralization is meant to establish the broad framework for decentralization/devolution, leaving the detailed planning, operationalization and monitoring of the process to the lower levels of administration. The Alma Ata Declaration on Health for All acknowledges the role of decentralisation in public service delivery. Within the framework of decentralization, officials operate within an actual or perceived area of autonomy which is characterized as their decision space. The assumption is that within the boundaries of this decision space, officials are able to make innovative choices that promote their/the systems efficiency and effectiveness, and thereby enhance the achievement of measurable health outcomes. Methodology: The study builds on an initial pilot study in Tumkur district and extends it to an additional 6 districts of Karnataka, in a combination of developed and backward districts based on the Nanjundappa Committee report. The study includes both qualitative and quantitative data generated from questionnaires, interviews and focus group discussions at the district, taluk and panchayats levels. For the quantitative analysis, we designed a decision space questionnaire administered to a range of technocrats, bureaucrats and people’s representatives at the district level and below. The questions relate to the respondents’ perceptions of power at their disposal. Qualitative data will include interviews with the above-mentioned officials. Data pertaining to health outcome indicators taken from DLHS-3 and state NRHM program implementation plans are then analysed in relation to the perceived decision-space. Findings and Recommendations: The study demonstrates the relationship between perceived decision space and the effectiveness of the health system; and also provides insights into the impact of decentralization in diverse settings. The pilot study showed that: Decision space overall is limited among all officials at the district level; and the limitation is highest in matters relating to human resources, planning and budgeting. Program Officers appeared to have greater decision space, and greater control within the specific boundaries of their programs. People’s representatives have little influence over decisions made by the Health Department; however, they can contribute with their own funds to specific activities such as providing additional drugs or civil works. The government needs to spell out in greater detail the exact activities which can be devolved to lower levels of administration; and provide the financial and operational autonomy to bring about genuine empowerment at those levels. Officials need to be trained to be able to carry out the responsibilities allocated to them and to understand better what they can and cannot do under the framework of decentralized decision making.

Sentiment analysis is one of the most promising research areas in the field of data analysis. In sentiment analysis we do classification of the data samples into positive, negative or neutral classes and bring out proper conclusions from the data. From movie reviews to the twitter data and many others we can impose sentiment analysis. There are many classification models that can be used for sentiment analysis. Some of these models are effective in doing the analysis and some are not. Though the effectiveness and efficiency of the analysis depend on many factors one of the major factors is the underlying feature selection process of the classification models. The features are the attributes of the data samples. Instead of classify the data samples based on the complete set of attributes one can build a model which uses the subset of attribute. Our proposed framework integrates a metaheutristic optimization technique which is a variation of Particle Swarm Optimization which we have named as RAPID-PSO with the multiple classification models which extract a subset of features from the complete set of features. Based on this sub set of features the framework farther classifies the samples. The result shows that our proposed frame work outperforms the other frameworks that use other classical optimization techniques such as –Grid search, Gradient Descend, Classical-PSO, Multiple-PSO, IPSO in terms of effectiveness and efficiency.

Network measurement based modeling and optimization for IP geolocation

Value chain analysis is an important tool to assess and enhance the performance of agribusiness. This paper analyzes the empirical application of a conceptual framework known as the Rural Web to evaluate the socioeconomic complexity of a specific agribusiness value chain. This can be used as a complementary approach to traditional value chain analysis. The proposed framework goes beyond linear descriptions of product flows and examines how supply chains are built, shaped and reproduced over time and space, while considering social, cultural, environmental and political aspects. The results demonstrate that the proposed framework is a suitable method for value chain analysis, principally for those whose early stages are based on small and medium-sized rural actors. The Rural Web analysis offers decision-makers a platform to identify key actors not traditionally considered in value chain analysis, as well as the social interrelationships that occur at different dimensions. It also enables the identification of corrective and preventive measures to enhance agribusiness value chains.

In this paper, a novel haptic interaction framework for deformable objects is introduced and discussed. Unlike existing frameworks, the proposed approach is based on point-based mechanics framework. Contrary to the finite-element method, the method of this paper does not rely on any predefined mesh representation, and it also incorporates real material properties in the modeling framework. As a result, the haptic interaction produces realistic model behaviour and force feedback. In addition, the framework can be constructed using the actual 3D volume data obtained from various imaging modalities. In this paper, preliminarily implementation issues of this new frame work are discussed and experimental data are compared to the FEM.

The goal of this study is to develop churn models for sellers on the e-commerce marketplace by using machine learning methods. In order to develop these models, three approaches are applied for developing the models. The dataset used in this study includes ten features, which are maturity type, maturity interval, city of the seller, total revenue of the seller, total transaction of the seller, sector type of the seller, business type of the seller, sales channel, installment option and discount type. Random Forest (RF) and Logistic Regression (LR) are used for churn analysis in all of the approaches. In the first approach, models are developed without applying preprocessing operations on the dataset. In the second and third approaches, under sampling and oversampling methods are used respectively to balance the data set. By using stratified cross validation on the dataset, F-Scores of the churn models are obtained. The results show that F-Scores were 0.76, 0.71 and 0.92 for the three approaches developed with RF, and 0.84, 0.68 and 0.69 for the three approaches developed with LR, respectively.

The main objective of this paper is to present first and second-order sensitivity analysis of vibro-acoustic systems in the statistical energy analysis (SEA) frame work. Equations for computing these sensitivities for a general SEA model are obtained from two different approaches: (1) direct method and (2) adjoint method. The above equations are applied to a simple model of three plates, joined in the form of a ‘Z’, to minimize the total energy of one of the plates. It has been verified that these approaches lead to the same results and the difference between them is only with respect to the computational efficiency. The design sensitivity results calculated from the proposed analytical methods are compared with those obtained from the finite difference method, which show good agreement. The results of this paper can be useful to optimization of vibro-acoustic systems at the drawing board stage in the SEA framework.

1. Attributes of Economic Indicators Business Cycles Seasonality Index Numbers Data Accuracy Calculating and Presenting Growth Rates Price-Adjusted Dollars The Underground Economy Distinctions Among and Structures The Production of Economic Statistics Data Integrity Review Questions Notes 2. Framework for Macroeconomic Analysis, Policies, and Forecasting Analytic System Fiscal, Monetary, and Incomes Policies Humphrey-Hawkins Act Unemployment and Inflation Goals: Fiscal and Monetary Policies Incomes Policies Review Questions Notes 3. Economic Growth GDP Methodology Analysis of Trends Assessment of Economic Growth in the First Decade of the 21st Century An Action Plan Review Questions Notes 4. Household Income and Expenditure Methodology Analysis of Trends Consumer Optimism and Pessimism Opinion Surveys Review Questions Notes 5. Business Profits, Nonresidential Investment, and Housing GDP, Nonresidential Investment, and Business Profits Housing Review Questions Notes 6. Government Budget Aggregates and Components Prospects for Government Surplus/Deficit Position in the Mid-2000s Taxes and Work Effort Review Questions Notes 7. The United States in the World Economy International Transaction Methodology Demand for Exports and Imports Value of the U.S. Dollar Globalization of Production Goods, Services, and Income Balances Investment Position of the U.S. Sustainability of Deficits and Debtor Status in the Balance of Payments Review Questions Notes 8. Employment, Worker Income, and Employer Costs Employment Methodology of the Two Employment Surveys Analysis of Employment Trends Analysis of Employment Trends Worker Income Employer Costs Review Questions Notes 9. Unemployment Unemployment Data Methodology of Measuring Unemployment Analysis of Trends Unemployment Insurance Review Questions Notes 10. Productivity Methodology of Measuring Productivity Analysis of Trends Productivity and Price Change Review Questions Notes 11. Inflation and Deflation Definitions CPI Methodology Analysis of Trends Concern About Deflation: Common Stock and Housing Prices and the CPI Inflation and Capacity Utilization Review Questions Notes 12. Finance Implementing Monetary Policies Interest Rates The Stock Market Appendix: Federal Reserve Organization and Independence Review Questions Notes 13. Leading Indicator System Overview The Process of Cyclical Change The Primary Role of Profits Limitation for Forecasting Calculation of the Composite Indexes Assessments of the Leading Indicator System Probability of Future Recessions: Experimental Indexes Review Questions Notes 14. Noneconomic Intangibles Review Question

The emphasis of this study is on the framework of economic and social effects of disaster situations rather than on disaster policy prescription. We prefer to concentrate on the analytical framework within which policy-making should be based and carried through rather than on actual policy-making. This frame work is also intended to facilitate a critical analysis of existing viewpoints on disasters, particularly in relation to the economy. The study refers especially to developing countries affected by large natural disasters in the last three decades, but as most prevailing views derive from disaster analysis in developed countries, especially the USA, the developed world is also well represented.

We propose an end-to-end graph similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks. Our proposed framework performs higher-order convolutions by incorporating higher-order proximity in graph convolutional networks to characterize and learn the community structure in brain connectivity networks. To the best of our knowledge, this is the first community-preserving graph similarity learning framework for multi-subject brain network analysis. Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders. Our proposed approach achieves an average AUC gain of $75$% compared to PCA, an average AUC gain of $65.5$% compared to Spectral Embedding, and an average AUC gain of $24.3$% compared to S-GCN across the four datasets, indicating promising applications in clinical investigation and brain disease diagnosis.

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. A novel and rigorous non-local formulation for flow in porous media is presented, in which the reservoir is represented by an adjacency matrix describing the connectivities of comprising compartments. We automatically divide the reservoir into a number of distinct compartments, in which the direction-dependent multiphase flow communication is a function of non-local phase potential differences. Our proposed clustering framework begins with a mixed-type raw dataset which can be categorical/numerical, spatial/temporal, and discrete/continuous. The dataset can contain noisy/missing values of different data types including but not limited to well production/injection history, well location, well type, geological features, PVT measurements, perforation data, etc. Unsupervised clustering techniques suited to the input data types (e.g. k-prototypes, spectral, Gaussian Mixtures, and hierarchical clustering), and appropriate distance measures (such as Euclidean distance, soft dynamic time warping, and mode) are used. The input data is standardized, and upon convergence check, the best clustering representation is obtained. Finally, Support-Vector-Machine technique is utilized in the kernel space to trace a demarcating hyperplane for the clusters. The proposed framework is successfully applied to more than five mature fields in the Middle East, South and North America, each with more than a thousand wells. In a specific case study reported here, the proposed workflow is applied to a major field with a couple of hundreds of wells with more than 40 years of production history. Leveraging the fast forward model, an efficient ensemble-based history matching framework is applied to reduce the uncertainty of the global reservoir parameters such as inter-blocks and aquifer-reservoir communications, fault transmissibilities, and block-based oil in place. The ensemble of history matched models are then used to provide a probabilistic forecast for different field development scenarios. In addition, the clustering framework enables us to treat missing data and use the augmented dataset for improving the clustering accuracy. In summary, in this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome.

In the era of the information economic development of society, which is based on the digitalization of communication technologies and software for business administration algorithms, business analytics occupies an important place, a tool for obtaining high-quality, unique, and expensive information, maintaining the global competitive advantages of projects in the face of long-term force majeure risks of the viability of the economy. Since the middle of the last century, information technologies have been in constant development, gradually being introduced into all spheres and strata of society: production, accounting and analytical support, management, analysis, forecasts, risks. This paper considers the tools of digital business transformation and trends within the new knowledge paradigm based on a controlled understanding of the value properties of an information product. The successful use of business intelligence can indicate an increase in the success of the company and the country's competitiveness in the global market. Business analytics of companies is currently undergoing a stage of reengineering due to testing various programs, understanding, and analyzing the database, improving communication interaction at all levels of business processes, and making decisions in conditions of interactivity and transparency. The state of traceability and transparency of information, evolution and dynamics of conclusions and proposals over a long period of the company's activity in the sectoral and global aspect is of value. The features, characteristics, and functions of business intelligence (BI) systems as a tool for increasing the efficiency of the company's activities are analyzed. Business intelligence systems were considered as a tool for data analysis and processing, a comparative analysis of modern business intelligence systems and market trends of business intelligence systems was conducted, because of which the role of BI systems in increasing the efficiency of the company's activities was revealed. Keywords: business analytics, business data analysis, data integration, data presentation, IT sphere, company performance.

One of the most important problems of telecommunication companies is the potential transfer of customers between the firms. In order to avoid this problem, it is very important to identify customers who are likely to leave. In this study, the performance of the classification and the clustering algorithms in machine learning techniques has been evaluated and compared on the analysis of potential customer trends, which have been reported as churn analysis. K nearest neighbors, decision trees, random forests, support vector machines and naive bayes methods were tested in scope of classification idea. Additionally, K-Means and hierarchical clustering methods were tested. The performances of the methods have been evaluated according to the accuracy, precision, sensitivity and F-measure performance metrics.

Trend analyses are the necessary tools for depicting possible general increase or decrease in a given time series. There are many versions of trend identification methodologies such as the Mann-Kendall trend test, Spearman’s tau, Sen’s slope, regression line, and Şen’s innovative trend analysis. The literature has many papers about the use, cons and pros, and comparisons of these methodologies. In this paper, a completely new approach is proposed based on the crossing properties of a time series. It is suggested that the suitable trend from the centroid of the given time series should have the maximum number of crossings (total number of up-crossings or down-crossings). This approach is applicable whether the time series has dependent or independent structure and also without any dependence on the type of the probability distribution function. The validity of this method is presented through extensive Monte Carlo simulation technique and its comparison with other existing trend identification methodologies. The application of the methodology is presented for a set of annual daily extreme rainfall time series from different parts of Turkey and they have physically independent structure.