Data Paper Research Scrubbing

Data Paper Research Scrubbing-73
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.

Tags: What Is A Business PlanningAdvertisement Analysis Essay OutlineEditing Your Own EssayEthan Frome Isolation EssayKite Runner Theme EssayInternational Business Plan ExampleEntrance Scholarship EssayHow To Write An Evaluation Essay On A MovieClassical Argument Outline For Research Paper

Either comparable facilities need to be provided by national science foundations or vendors need to be persuaded to introduce the concept of an ‘educational license.’Clearly, there is a large and increasing number of (commercial) services providing access to social networking media (e.g., Twitter, Facebook and Wikipedia) and news services (e.g., Thomson Reuters Machine Readable News). We start by discussing types of data and formats produced by these services.

Although we focus on social media, as discussed, researchers are continually finding new and innovative sources of data to bring together and analyze.

Three illustrative areas are: business, bioscience and social science.

The early business adopters of social media analysis were typically companies in retail and finance.

Currently, social media data is typically either available via simple general routines or require the researcher to program their analytics in a language such as MATLAB, Java or Python.

As discussed above, researchers require: There are an increasing number of powerful commercial platforms, such as the ones supplied by SAS and Thomson Reuters, but the charges are largely prohibitive for academic research.The majority of social media resources are commercial and companies are naturally trying to monetize their data.As discussed, it is important that researchers have access to open-source ‘big’ (social media) data sets and facilities for experimentation.Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services.This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms.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.So when considering textual data analysis, we should consider multiple sources (e.g., social networking media, RSS feeds, blogs and news) supplemented by numeric (financial) data, telecoms data, geospatial data and potentially speech and video data.Using multiple data sources is certainly the future of analytics.In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London.The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business.

SHOW COMMENTS

Comments Data Paper Research Scrubbing

  • Data Cleansing to Improve Data Analysis Trifacta
    Reply

    Trifacta’s unique approach to data cleansing. Data cleansing is the first step in the overall data preparation process and is the process of analyzing, identifying and correcting messy, raw data. When analyzing organizational data to make strategic decisions you must start with a thorough data cleansing process.…

  • Problems, Methods, and Challenges in Comprehensive Data Cleansing
    Reply

    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.…

  • 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…

  • 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…

  • 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.…

  • 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.…

  • 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…

  • PDF A Clean-Slate Look at Disk Scrubbing. - Share and discover research
    Reply

    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.…

The Latest from www.foxtrot-fab.ru ©