Data Paper Research Scrubbing

Data Paper Research Scrubbing-17
Wolfram () used Twitter data to train a Support Vector Regression (SVR) model to predict prices of individual NASDAQ stocks, finding ‘significant advantage’ for forecasting prices 15 min in the future.In the biosciences, social media is being used to collect data on large cohorts for behavioral change initiatives and impact monitoring, such as tackling smoking and obesity or monitoring diseases.

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They either give superficial access to the raw data or (for non-superficial access) require researchers to program analytics in a language such as Java.

Social media data is clearly the largest, richest and most dynamic evidence base of human behavior, bringing new opportunities to understand individuals, groups and society.

For completeness, HTML and XML are so-called markup languages (markup and content) that define a set of simple syntactic rules for encoding documents in a format both human readable and machine readable.

A markup comprises start-tags (e.g., Many feeds use Java Script Object Notation (JSON), the lightweight data-interchange format, based on a subset of the Java Script Programming Language.

Innovative scientists and industry professionals are increasingly finding novel ways of automatically collecting, combining and analyzing this wealth of data.

Naturally, doing justice to these pioneering social media applications in a few paragraphs is challenging.

For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis.

Although principally a review, the paper also provides a of social media tools.

Otherwise, social media research could become the exclusive domain of major companies, government agencies and a privileged set of academic researchers presiding over private data from which they produce papers that cannot be critiqued or replicated.

Recently, there has been a modest response, as Twitter and Gnip are piloting a new program for data access, starting with 5 all-access data grants to select applicants.

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Comments Data Paper Research Scrubbing

  • Problems, Methods, and Challenges in Comprehensive Data Cleansing
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    All these under the term data cleansing; other names are data cleaning, scrubbing, or recon-ciliation. There is no common description about the objectives and extend of comprehensive data cleansing. Data cleansing is applied with varying comprehension and demands in the different areas of data processing and maintenance.…

  • PDF A Clean-Slate Look at Disk Scrubbing. - Share and discover research
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    A Clean-Slate Look at Disk Scrubbing. none of these approaches has been evaluated on real field data. This paper makes two contributions. Join ResearchGate to find the people and research.…

  • Data Cleaning Detecting, Diagnosing, and Editing Data Abnormalities
    Reply

    The History of Data Cleaning. With Good Clinical Practice guidelines being adopted and regulated in more and more countries, some important shifts in clinical epidemiological research practice can be expected. One of the expected developments is an increased emphasis on standardization, documentation, and reporting of data handling and data.…

  • A Monthly Journal of Computer Science and Information Technology
    Reply

    Or inconsistent data can lead to false conclusion and misdirect investment on both public and private scale. Data comes from various systems and in many different forms. It may be incomplete, yet it is a raw material for data mining. This research paper provides an overview of data cleaning problems, data quality, cleaning approaches…

  • Data Cleaning Problems and Current Approaches
    Reply

    Data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data. Data quality problems are present in single data collections, such as files and databases, e.g. due to misspellings during data entry, missing information or other invalid data.…

  • Data Masking Best Practice White Paper -
    Reply

    Values. This allows data to be safely used in non-production and incompliance with regulatory requirements such as Sarbanes-Oxley, PCI DSS, HIPAA and as well as numerous other laws and regulations. This paper describes the best practices for deploying Oracle Data Masking to protect sensitive…

  • A Comparison Study of Data Scrubbing Algorithms and Frameworks in Data.
    Reply

    Quality in the data warehouse, This paper focus on Data Quality in ETL stage, one of the major steps of ETL stage is Data Scrubbing. Data scrubbingDS is the first important pre-process step and most critical in a Business Intelligence BI or Data warehousing project 5. To have High quality data, all…

  • The Challenges of Data Quality and Data Quality Assessment in the Big.
    Reply

    First, this paper summarizes reviews of data quality research. Second, this paper analyzes the data characteristics of the big data environment, presents quality challenges faced by big data, and formulates a hierarchical data quality framework from the perspective of data users.…

  • Quantitative Data Cleaning for Large Databases - Berkeley Database Research
    Reply

    Quantitative data are integers or oating point numbers that measure quantities of interest. Quantitative data may consist of simple sets of numbers, or complex arrays of data in multiple dimensions, sometimes captured over time in time series. Quantitative data is typically based in some unit of measure, which needs to be uniform across the data…

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