The size and share of this market is categorized based on Data Entry Services (Online Data Entry, Offline Data Entry, Image Data Entry, Database Entry, Data Conversion) and Data Processing Services (Data Cleaning, Data Mining, Data Validation, Data Analysis, Data Integration) and Document Management Services (Document Digitization, Document Indexing, Document Scanning, Document Archiving, Document Retrieval) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

De marktomvang en het marktaandeel zijn gecategoriseerd op basis van Data Entry Services (Online Data Entry, Offline Data Entry, Image Data Entry, Database Entry, Data Conversion) and Data Processing Services (Data Cleaning, Data Mining, Data Validation, Data Analysis, Data Integration) and Document Management Services (Document Digitization, Document Indexing, Document Scanning, Document Archiving, Document Retrieval) and geografische regio’s (Noord-Amerika, Europa, Azië-Pacific, Zuid-Amerika, Midden-Oosten en Afrika)

Vedant, S., Silber, E. A. (2025) An Exploratory Data Mining Study of Re-entry Events: Foundations for Multi-Modality Sensing and Data Fusion, Sandia National Laboratories report

O tamanho e a participação do mercado são categorizados com base em Data Entry Services (Online Data Entry, Offline Data Entry, Image Data Entry, Database Entry, Data Conversion) and Data Processing Services (Data Cleaning, Data Mining, Data Validation, Data Analysis, Data Integration) and Document Management Services (Document Digitization, Document Indexing, Document Scanning, Document Archiving, Document Retrieval) and regiões geográficas (América do Norte, Europa, Ásia-Pacífico, América do Sul, Oriente Médio e África)

The Data Processing & Hosting Services Market report segments the industry into By Organisation (Large Enterprise, Small & Medium Enterprise (SME)), By Offering (Data Processing Services, Hosting Services), By End-User Industry (IT & Telecommunication, BFSI, Retail, Other End-user Industries), and By Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa).

The presented data is the input big data structured information considering suppliers of ICT sector in MAMUT Corporations. The big data input is structured from 20 business areas and 479 suppliers. The big data decision matrix is based on 7 criteria which have been identified by library research and expert comments and the matrix completed by expert comments. Other files are the detailed results of the CLUS-MCDA algorithm process.

이 시장의 규모와 점유율은 다음을 기준으로 분류됩니다: Data Entry Services (Online Data Entry, Offline Data Entry, Image Data Entry, Database Entry, Data Conversion) and Data Processing Services (Data Cleaning, Data Mining, Data Validation, Data Analysis, Data Integration) and Document Management Services (Document Digitization, Document Indexing, Document Scanning, Document Archiving, Document Retrieval) and 지역별 (북미, 유럽, 아시아 태평양, 남미, 중동 및 아프리카)

The Commercial Remote Data Processing Services market is experiencing significant growth as businesses increasingly rely on external expertise to enhance their data management capabilities. This sector encompasses a wide range of services, including data entry, data conversion, data mining, and data warehousing, all

The "Dataset_HIR" folder contains the data to reproduce the results of the data mining approach proposed in the manuscript titled "Identification of hindered internal rotational mode for complex chemical species: A data mining approach with multivariate logistic regression model".

More specifically, the folder contains the raw electronic structure calculation input data provided by the domain experts as well as the training and testing dataset with the extracted features.

The "Dataset_HIR" folder contains the following subfolders namely:

1. Electronic structure calculation input data: contains the electronic structure calculation input generated by the Gaussian program

   1.1. Testing data: contains the raw data of all training species (each is stored in a separate folder) used for extracting dataset for training and validation phase.

   1.2. Testing data: contains the raw data of all testing species (each is stored in a separate folder) used for extracting data for the testing phase.

2. Dataset 2.1. Training dataset: used to produce the results in Tables 3 and 4 in the manuscript

   + datasetTrain_raw.csv: contains the features for all vibrational modes associated with corresponding labeled species to let the chemists select the Hindered Internal Rotor from the list easily for the training and validation steps.  
   
   + datasetTrain.csv: refines the datasetTrain_raw.csv where the names of the species are all removed to transform the dataset into an appropriate form for the modeling and validation steps.
   

   2.2. Testing dataset: used to produce the results of the data mining approach in Table 5 in the manuscript.

   + datasetTest_raw.csv: contains the features for all vibrational modes of each labeled species to let the chemists select the Hindered Internal Rotor from the list for the testing step.
   
   + datasetTest.csv: refines the datasetTest_raw.csv where the names of the species are all removed to transform the dataset into an appropriate form for the testing step.
   

Note for the Result feature in the dataset: 1 is for the mode needed to be treated as Hindered Internal Rotor, and 0 otherwise.

With health care policy directives advancing value-based care, risk assessments and management have permeated health care discourse. The conventional problem-based infrastructure defines what data are employed to build this discourse and how it unfolds. Such a health care model tends to bias data for risk assessment and risk management toward problems and does not capture data about health assets or strengths. The purpose of this article is to explore and illustrate the incorporation of a strengths-based data capture model into risk assessment and management by harnessing data-driven and person-centered health assets using the Omaha System. This strengths-based data capture model encourages and enables use of whole-person data including strengths at the individual level and, in aggregate, at the population level. When aggregated, such data may be used for the development of strengths-based population health metrics that will promote evaluation of data-driven and person-centered care, ou...

Average performance of minimal cover rule classifiers induced from discrete input data for supervised discretisation [%].

Data set associated with the Astroturfing study as a strategy for manipulating public opinion on Twitter during the pandemic in Spain.There are two files:- Phillipines_16062020.RData, with the first round of data collection, was carried out in the study carried out, with all the tweets during about three days. This database was used to identify users considered in the study.- Phillipines_280cuentas.RData, with the tweets collected from the selected accounts from the analysis of the data collected in Phillipines_16062020.RData, for almost four months.User identification variables were eliminated due to the privacy conditions of Twitter that allow the analysis of groups or public figures, but not of individuals.

China Input-Output: Intermediate Use: Total Input: Mining data was reported at 5,383.521 RMB bn in 2015. This records an increase from the previous number of 5,359.810 RMB bn for 2012. China Input-Output: Intermediate Use: Total Input: Mining data is updated yearly, averaging 1,947.033 RMB bn from Dec 1995 (Median) to 2015, with 9 observations. The data reached an all-time high of 5,383.521 RMB bn in 2015 and a record low of 545.676 RMB bn in 1995. China Input-Output: Intermediate Use: Total Input: Mining data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AG: Input-Output Table: Intermediate Use: Input/Output: Mining.

China Input-Output: Intermediate Use: Intermediate Input: Mining: Electricity, Heating and Water Production and Supply data was reported at 329.861 RMB bn in 2015. This records an increase from the previous number of 312.883 RMB bn for 2012. China Input-Output: Intermediate Use: Intermediate Input: Mining: Electricity, Heating and Water Production and Supply data is updated yearly, averaging 200.372 RMB bn from Dec 1995 (Median) to 2015, with 9 observations. The data reached an all-time high of 360.879 RMB bn in 2010 and a record low of 27.119 RMB bn in 1995. China Input-Output: Intermediate Use: Intermediate Input: Mining: Electricity, Heating and Water Production and Supply data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AG: Input-Output Table: Intermediate Use: Input/Output: Mining.

This is a support Java program for generating input files for Higher Order Neural Network learning program

This file is in Excel (xls) format, and contains data about regression model for input and output parameters (constants) that can be used for the solving of real-world vehicle routing problems with realistic non-standard constraints. All data are real and obtained experimentally by using VRP algorithm on production environment in one of the biggest distribution companies in Bosnia and Herzegovina.

This dataset is essentially the metadata from 164 datasets. Each of its lines concerns a dataset from which 22 features have been extracted, which are used to classify each dataset into one of the categories 0-Unmanaged, 2-INV, 3-SI, 4-NOA (DatasetType).

This Dataset consists of 164 Rows. Each row is the metadata of an other dataset. The target column is datasetType which has 4 values indicating the dataset type. These are:

2 - Invoice detail (INV): This dataset type is a special report (usually called Detailed Sales Statement) produced by a Company Accounting or an Enterprise Resource Planning software (ERP). Using a INV-type dataset directly for ARM is extremely convenient for users as it relieves them from the tedious work of transforming data into another more suitable form. INV-type data input typically includes a header but, only two of its attributes are essential for data mining. The first attribute serves as the grouping identifier creating a unique transaction (e.g., Invoice ID, Order Number), while the second attribute contains the items utilized for data mining (e.g., Product Code, Product Name, Product ID).

3 - Sparse Item (SI): This type is widespread in Association Rules Mining (ARM). It involves a header and a fixed number of columns. Each item corresponds to a column. Each row represents a transaction. The typical cell stores a value, usually one character in length, that depicts the presence or absence of the item in the corresponding transaction. The absence character must be identified or declared before the Association Rules Mining process takes place.

4 - Nominal Attributes (NOA): This type is commonly used in Machine Learning and Data Mining tasks. It involves a fixed number of columns. Each column registers nominal/categorical values. The presence of a header row is optional. However, in cases where no header is provided, there is a risk of extracting incorrect rules if similar values exist in different attributes of the dataset. The potential values for each attribute can vary.

0 - Unmanaged for ARM: On the other hand, not all datasets are suitable for extracting useful association rules or frequent item sets. For instance, datasets characterized predominantly by numerical features with arbitrary values, or datasets that involve fragmented or mixed types of data types. For such types of datasets, ARM processing becomes possible only by introducing a data discretization stage which in turn introduces information loss. Such types of datasets are not considered in the present treatise and they are termed (0) Unmanaged in the sequel.

The dataset type is crucial to determine for ARM, and the current dataset is used to classify the dataset's type using a Supervised Machine Learning Model.

There is and another dataset type named 1 - Market Basket List (MBL) where each dataset row is a transaction. A transaction involves a variable number of items. However, due to this characteristic, these datasets can be easily categorized using procedural programming and DoD does not include instances of them. For more details about Dataset Types please refer to article "WebApriori: a web application for association rules mining". https://link.springer.com/chapter/10.1007/978-3-030-49663-0_44

Размер и доля сегментированы по Data Processing Services (Data Entry Services, Data Mining Services, Data Analytics Services, Data Integration Services, Data Management Services) and Hosting Services (Shared Hosting, Dedicated Hosting, VPS Hosting, Cloud Hosting, Managed Hosting) and Cloud Services (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Backup and Recovery Services, Disaster Recovery Services) and регионам (Северная Америка, Европа, Азиатско-Тихоокеанский регион, Южная Америка, Ближний Восток и Африка)

The dataset presents input/output data of the TGOWLeR framework. TGOWLeR abstracts general patterns from workflow sequences previously extracted from texts. It comprises two modules –a workflow extractor and a pattern miner– both relying on a specific domain ontology.The input of the first module is an RDF/ZIPPED ontology (tgowler_resource_ontologies_PHAGE_1.0.rdf.zip) and a set of un-annotated articles (e.g., datastore_2018_2019.zip). The proposed pipeline (implemented in GATE 8.1) produces annotated texts (e.g., annotated_2018_2019.zip) and their corresponding workflows (e.g., WFMiner_WF_2018_2019.xml).The input of the second module is the serialized ontology ... Publications:Halioui, Ahmed, et al. "Ontology-based workflow pattern mining: Application to bioinformatics expertise acquisition." Proceedings of the Symposium on Applied Computing. ACM, 2017.Halioui, Ahmed, Petko Valtchev, and Abdoulaye Baniré Diallo. "Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation." IEEE/ACM transactions on computational biology and bioinformatics 15.6 (2018): 1979-1990.

Input data considered for the biomedical research assimilator context.