Data Retrieval and Sorting for Multidimensional Data Using Machine Learning in Big Data

How to retrieve accurately and quickely locate information from massive network data from the big data is focuse on data retrieve sorting optimization model.Based on traditional data retrieval sorting technology this work proposes multidimensional data retrieval and sorting considering the characteristic of data,users and application. This work use the financial microblog data retrieval base on real query intensions and financial tendency of the system. Finally this work shows the test results for multidimensional data retrieval and sorting using machine learning. In this thesis, proposed methodology has been presented. The proposed system is implemente using machine learning based on the multidimensional data characteristic. This proposed system is implemented in java . It shows soritng and retrieval result of the system based on the characteristic of multidimensional data

Performance Pay and Multidimensional Sorting - Productivity, Preferences and Gender

This paper studies the impact of incentives on worker self-selection in a controlled laboratory experiment. Subjects face the choice between a fixed and a variable payment scheme. Depending on the treatment, the variable payment is a piece rate, a tournament or a revenue-sharing scheme. We find that output is higher in the variable pay schemes (piece rate, tournament, and revenue sharing) compared to the fixed payment scheme. This difference is largely driven by productivity sorting. In addition personal attitudes such as willingness to take risks and relative self-assessment as well as gender affect the sorting decision in a systematic way. Moreover, self-reported effort is significantly higher in all variable pay conditions than in the fixed wage condition. Our lab findings are supported by an additional analysis using data from a large and representative sample. In sum, our findings underline the importance of multi-dimensional sorting, i.e., the tendency for different incentive schemes to systematically attract people with different individual characteristics

Performance Pay and Multidimensional Sorting - Productivity, Preferences and Gender

This paper studies the impact of incentives on worker self-selection in a controlled laboratory experiment. Subjects face the choice between a fixed and a variable payment scheme. Depending on the treatment, the variable payment is a piece rate, a tournament or a revenue-sharing scheme. We find that output is higher in the variable pay schemes (piece rate, tournament, and revenue sharing) compared to the fixed payment scheme. This difference is largely driven by productivity sorting. In addition personal attitudes such as willingness to take risks and relative self-assessment as well as gender affect the sorting decision in a systematic way. Moreover, self-reported effort is significantly higher in all variable pay conditions than in the fixed wage condition. Our lab findings are supported by an additional analysis using data from a large and representative sample. In sum, our findings underline the importance of multi-dimensional sorting, i.e., the tendency for different incentive schemes to systematically attract people with different individual characteristics.Sorting; Incentives; Piece Rates; Tournament; Revenue-Sharing; Risk Preferences; Social Preferences; Gender; Experiment; Field Evidence

Multidimensional Skills, Sorting, and Human Capital Accumulation

We construct a structural model of on-the-job search in which workers differ in skills along several dimensions and sort themselves into jobs with heterogeneous skill requirements along those same dimensions. Skills are accumulated when used, and depreciate when not used. We estimate the model combining data from O*NET with the NLSY79. We use the model to shed light on the origins and costs of mismatch along heterogeneous skill dimensions. We highlight the deficiencies of relying on a unidimensional model of skill when decomposing the sources of variation in the value of lifetime output between initial conditions and career shocks

A MODEL OF ENDOGENOUS POLITICAL PARTY PLATFORMS

We develop a model of endogenous party platform formation in a multidimensional policy space. Party platforms depend on the composition of the parties? primary electorate. The overall social outcome is taken to be a weighted average of party platforms and individuals vote strategically. Equilibrium is defined to obtain when no group of voters can shift the social outcome in its favor by deviating and the party platforms are consistent with their electorate. We provide sufficient conditions for existence and study the robustness properties of the sorting equilibria.Political Economy, Endogenous Party Formation, Multimensional Space.

Developing priorities to achieve health equity through diabetes translation research: A concept mapping study

Introduction: The goal of diabetes translation research is to advance research into practice and ensure equitable benefit from scientific evidence. This study uses concept mapping to inform and refine future directions of diabetes translation research with the goal of achieving health equity in diabetes prevention and control. Research design and methods: This study used concept mapping and input from a national network of diabetes researchers and public health practitioners. Concept mapping is a mixed-method, participant-based process. First, participants generated statements by responding to a focus prompt ( Results: Ten clusters were identified containing between 6 and 12 statements from 95 total generated statements. The ranges of average importance and feasibility ratings for clusters were fairly high and narrow (3.62-4.09; 3.10-3.93, respectively). Clusters with the most statements in the go-zone quadrant (above average importance/feasibility) were Conclusions: This study created a framework of 10 priority areas to guide current and future efforts in diabetes translation research to achieve health equity. Themes rated as highly important and feasible provide the basis to evaluate current research support. Future efforts should explore how to best support innovative-targets, those rated highly important but less feasible