In this research, data mining and decision tree techniques were analyzed as well as the induction of rules to integrate their many algorithms into the database managing system (DBMS), PostgreSQL, due to the defficiencies of the free use tools avaialable. A mechanism to optimize the performance of the implemented algorithms was proposed with the purpose of taking advantage of the PostgreSQL. By means of an experiment, it was proven that the time response and results obtained are improved when the algorithms are integrated into the managing system.

This is a set of spatial data sets which can use for discovering spatial co-location patterns.

Data Analytics Market Valuation – 2024-2031

Data Analytics Market was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2031, growing at a CAGR of 30.41% from 2024 to 2031.

Data Analytics Market Drivers

Data Explosion: The proliferation of digital devices and the internet has led to an exponential increase in data generation. Businesses are increasingly recognizing the value of harnessing this data to gain competitive insights.

Advancements in Technology: Advancements in data storage, processing power, and analytics tools have made it easier and more cost-effective for organizations to analyze large datasets.

Increased Business Demand: Businesses across various industries are seeking data-driven insights to improve decision-making, optimize operations, and enhance customer experiences.

Data Analytics Market Restraints

Data Quality and Integrity: Ensuring the accuracy, completeness, and consistency of data is crucial for effective analytics. Poor data quality can hinder insights and lead to erroneous conclusions.

Data Privacy and Security Concerns: As organizations collect and analyze sensitive data, concerns about data privacy and security are becoming increasingly important. Breaches can have significant financial and reputational consequences.

Advances in computing power and expansion of the Internet have led to increasing optimism that big data will lead to new insights. However, in the life sciences, relevant data is not only "big"; it is also highly decentralized across thousands of online databases. Wringing value from it depends on the discipline of data science and on the humble bricks and mortar that make it possible -- identifiers. However, our collective handling of identifiers has lagged behind these advances. Diverse identifier problems (for instance broken links and ‘content drift’) make it difficult to integrate data and derive new knowledge from it. This is a snapshot of a living document intended to show real-world examples of identifier problems representative of those encountered by data integrators. It is not meant to be exhaustive.

COAL_MINE_ENTRIES_DNR_IN.SHP contains the entrance locations of all documented underground coal mine entrances that have operated in the coal region of Indiana since the mid-1880s, up to December 31, 2016. COAL_MINE_ENTRIES_DNR_IN.SHP is attributed to allow the mine entrances to be differentiated based on entrance type (hoist shaft, other shafts, slope, unknown), depth, mine number, and source information (map number). The following is excerpted from the metadata provided by IDNR, Division of Reclamation, for the source point feature class named "COAL_ENTRY_GEOREF": "Coal_Mine_Entries_DNR_IN is a compilation of all documented underground coal mine entrances in Indiana. Coal_Mine_Entries_DNR_IN was compiled by the Indiana Geological Survey (IGS) as part of a contract deliverable for the Abandoned Mine Lands program of the Indiana Department of Natural Resources, Division of Reclamation. Coal_Mine_Entries_DNR_IN incorporates mine entrance locations compiled as part of the Indiana Coal Mine Information System (CMIS), an integrated geographic information system (GIS) and database management system (DBMS) created to store, analyze, and help distribute coal mine data in Indiana. The system contains data for surface and underground coal mines that operated in Indiana from the mid-1830s to 2007. Original source information for Coal_Mine_Entries_DNR_IN includes company mine maps, field maps and notes of IGS geologists, IGS publications, and Reports of the Indiana State Mine Inspector. All mine data included in Coal_Mine_Entries_DNR_IN are organized in a GIS using ESRI ArcGIS software of the Environmental Systems Research Institute (ESRI) on the Windows platform. Scale of source data ranges from 1:4,800 to 1:100,000."

PrefaceThis is the data repository for the paper accepted for publication in NUSA's special issue on Linguistic studies using large annotated corpora (co-edited by Hiroki Nomoto and David Moeljadi).How to cite the datasetIf you use, adapt, and/or modify any of the dataset in this repository for your research or teaching purposes (except for the malindo_dbase, see below), please cite as:Rajeg, Gede Primahadi Wijaya; Denistia, Karlina; Musgrave, Simon (2019): Dataset for Vector space model and the usage patterns of Indonesian denominal verbs. figshare. Fileset. https://doi.org/10.6084/m9.figshare.8187155.Alternatively, click on the dark pink Cite button to browse different citation style (default is DataCite).The malindo_dbase data in this repository is from Nomoto et al. (2018) (cf the GitHub repository). So please also cite their work if you use it for your research:Nomoto, Hiroki, Hannah Choi, David Moeljadi and Francis Bond. 2018. MALINDO Morph: Morphological dictionary and analyser for Malay/Indonesian. Kiyoaki Shirai (ed.) Proceedings of the LREC 2018 Workshop "The 13th Workshop on Asian Language Resources", 36-43.Tutorial on how to use the data together with the R Markdown Notebook for the analyses is available on GitHub and figshare:Rajeg, Gede Primahadi Wijaya; Denistia, Karlina; Musgrave, Simon (2019): R Markdown Notebook for Vector space model and the usage patterns of Indonesian denominal verbs. figshare. Software. doi: https://doi.org/10.6084/m9.figshare.9970205Dataset description1. Leipzig_w2v_vector_full.bin is the vector space model used in the paper. We built it using wordVectors package (Schmidt & Li 2017) via the MonARCH High Performance Computing Cluster (We thank Philip Chan for his help with access to MonARCH).2. Files beginning with ngramexmpl_... are data for the n-grams (i.e. words sequence) of verbs discussed in the paper. The files are in tab-separated format.3. Files beginning with sentence_... are full sentences for the verbs discussed in the paper (in the plain text format and R dataset format [.rds]). Information of the corpus file and sentence number in which the verb is found are included.4. me_parsed_nountaggedbase (in three different file-formats) contains database of the me- words with noun-tagged root that MorphInd identified to occur in three morphological schemas we focus on (me-, me-/-kan, and me-/-i). The database has columns for the verbs' token frequency in the corpus, root forms, MorphInd parsing output, among others.5. wordcount_leipzig_allcorpus (in three different file-formats) contains information on the size of each corpus file used in the paper and from which the vector space model is built.6. wordlist_leipzig_ME_DI_TER_percorpus.tsv is a tab-separated frequency list of words prefixed with me-, di-, and ter- in all thirteen corpus files used. The wordlist is built by first tokenising each corpus file, lowercasing the tokens, and then extracting the words with the corresponding three prefixes using the following regular expressions: - For me-: ^(?i)(me)([a-z-]{3,})$- For di-: ^(?i)(di)([a-z-]{3,})$- For ter-: ^(?i)(ter)([a-z-]{3,})$7. malindo_dbase is the MALINDO Morphological Dictionary (see above).ReferencesSchmidt, Ben & Jian Li. 2017. wordVectors: Tools for creating and analyzing vector-space models of texts. R package. http://github.com/bmschmidt/wordVectors.

in our study, we performed different analysis such as sentiment analysis on the tweets posted by the visitors of the most popular European museums before and after Covid-19 pandemic in order to understand how emotional responses of the users who are interested in art has changed by this global crisis.