Indonesia Big Data Analytics Software Market Analysis The Indonesia Big Data Analytics Software market is poised to witness substantial growth over the forecast period of 2025-2033, with a CAGR of 9.35%. In 2025, the market stood at a value of USD 43.15 million and is projected to reach a remarkable value by 2033. This growth is primarily driven by the increasing adoption of digital technologies, the proliferation of data-intensive applications, and the growing need for businesses to make data-driven decisions. Key trends shaping the market include the rising popularity of cloud-based big data analytics solutions, the emergence of advanced analytics techniques such as machine learning and artificial intelligence, and the growing awareness of data privacy and security concerns. Despite these positive factors, the market faces challenges such as the lack of skilled professionals in data analytics, the high cost of implementation, and the complexities associated with managing and integrating large volumes of data. Prominent players in the market include Teradata, SAS, SAP, Tableau Software, and IBM Corporation, among others. Market Size and Growth The Indonesia Big Data Analytics Software Market is projected to grow from USD 235.6 million in 2023 to USD 1,159.1 million by 2029, exhibiting a CAGR of 24.3% during the forecast period. This growth can be attributed to the increasing adoption of big data analytics solutions by organizations to enhance their decision-making, improve operational efficiency, and gain a competitive advantage. Recent developments include: June 2024: Indosat Ooredoo Hutchison (Indosat) and Google Cloud expanded their long-term alliance to accelerate Indosat’s transformation from telco to AI Native TechCo. The collaboration will combine Indosat’s vast network, operational, and customer datasets with Google Cloud’s unified AI stack to deliver exceptional experiences to over 100 million Indosat customers and generative AI (GenAI) solutions for businesses across Indonesia. These include geospatial analytics and predictive modeling, real-time conversation analysis, and back-office transformation. Indosat’s early adoption of an AI-ready data analytics platform exemplifies its forward-thinking approach., June 2024: Palo Alto Networks launched a new cloud facility in Indonesia, catering to the rising demand for local data residency compliance. The move empowers organizations in Indonesia with access to Palo Alto Networks' Cortex XDR advanced AI and analytics platform that offers a comprehensive security solution by unifying endpoint, network, and cloud data. With this new infrastructure, Indonesian customers can ensure data residency by housing their logs and analytics within the country.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

In my hometown, Suraj Breakfast Center stands out as a bustling culinary hub, drawing crowds from morning till evening. As a Data Analyst/Scientist, I often wondered about the sales and insights behind this local favourite. Given that the shop is owned by my friends relatives, it wasn't difficult for me to propose the idea of collecting sales data. From October until today, I've told my friend to gather end-of-day sales data for each dish.

As I prepare to share this dataset on Kaggle, I invite you to explore the sales insights of the shop. This collection is a simple, yet valuable, compilation of data obtained through a straightforward collaboration with the shop owners. Your engagement and upvotes will contribute to a better understanding of the local flavours that define Suraj Breakfast Center.

Here's what I've discovered.

1.Date:-From oct to nov

2.Dish name:- Dishes available in shop

3.Price:- Price of Dish per plate

4.Dine in:- They ate in shop only

5.Parcel:- They took the Parcel for home

6.Total Customers:- Total Customers for the individual dish for that day

7.Total Sales:- Total Sales of that day for that dish

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

The global business big data market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the proliferation of connected devices generating massive amounts of data, and the growing need for data-driven decision-making across various industries. The market's expansion is fueled by a surge in demand for advanced analytics, predictive modeling, and real-time data processing capabilities to optimize business operations, enhance customer experiences, and gain a competitive edge. While the exact market size for 2025 is unavailable, considering a plausible CAGR of 15% (a common growth rate for rapidly expanding technology sectors) and a starting point estimated at $150 billion in 2024, the market size in 2025 could reasonably be estimated around $172.5 billion. This growth is anticipated to continue into the forecast period (2025-2033), driven by factors such as increasing digital transformation initiatives across enterprises, the rise of artificial intelligence (AI) and machine learning (ML) applications, and the growing need for regulatory compliance involving data management and analysis. The market is segmented by application (individual users and enterprise users) and type (cloud-based and local-based). The enterprise user segment is currently dominating, owing to the higher data volumes and analytical needs of large organizations. Cloud-based solutions are experiencing faster growth due to their scalability, cost-effectiveness, and accessibility. Geographic distribution shows strong growth across North America and Asia Pacific, fueled by robust technological infrastructure and high levels of digital adoption in regions like the United States and China. However, growth is also expected in emerging economies driven by increasing internet and smartphone penetration and the adoption of big data technologies by a wider range of businesses. While challenges like data security concerns and the need for skilled professionals to manage and analyze big data present restraints, the overall market outlook remains strongly positive due to the transformative potential of big data analytics across various sectors.

The Local Analysis and Prediction System (LAPS), run by the NOAA's Forecast Systems Laboratory (FSL), combines numerous observed meteorological data sets into a collection of atmospheric analyses.

Introduction: I have chosen to complete a data analysis project for the second course option, Bellabeats, Inc., using a locally hosted database program, Excel for both my data analysis and visualizations. This choice was made primarily because I live in a remote area and have limited bandwidth and inconsistent internet access. Therefore, completing a capstone project using web-based programs such as R Studio, SQL Workbench, or Google Sheets was not a feasible choice. I was further limited in which option to choose as the datasets for the ride-share project option were larger than my version of Excel would accept. In the scenario provided, I will be acting as a Junior Data Analyst in support of the Bellabeats, Inc. executive team and data analytics team. This combined team has decided to use an existing public dataset in hopes that the findings from that dataset might reveal insights which will assist in Bellabeat's marketing strategies for future growth. My task is to provide data driven insights to business tasks provided by the Bellabeats, Inc.'s executive and data analysis team. In order to accomplish this task, I will complete all parts of the Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act). In addition, I will break each part of the Data Analysis Process down into three sections to provide clarity and accountability. Those three sections are: Guiding Questions, Key Tasks, and Deliverables. For the sake of space and to avoid repetition, I will record the deliverables for each Key Task directly under the numbered Key Task using an asterisk (*) as an identifier.

Section 1 - Ask: A. Guiding Questions: Who are the key stakeholders and what are their goals for the data analysis project? What is the business task that this data analysis project is attempting to solve?

B. Key Tasks: Identify key stakeholders and their goals for the data analysis project *The key stakeholders for this project are as follows: -Urška Sršen and Sando Mur - co-founders of Bellabeats, Inc. -Bellabeats marketing analytics team. I am a member of this team. Identify the business task. *The business task is: -As provided by co-founder Urška Sršen, the business task for this project is to gain insight into how consumers are using their non-BellaBeats smart devices in order to guide upcoming marketing strategies for the company which will help drive future growth. Specifically, the researcher was tasked with applying insights driven by the data analysis process to 1 BellaBeats product and presenting those insights to BellaBeats stakeholders.

Section 2 - Prepare: A. Guiding Questions: Where is the data stored and organized? Are there any problems with the data? How does the data help answer the business question?

B. Key Tasks: Research and communicate the source of the data, and how it is stored/organized to stakeholders. *The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through user Mobius in an open-source format. Therefore, the data is public and available to be copied, modified, and distributed, all without asking the user for permission. These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk reportedly (see credibility section directly below) between 03/12/2016 thru 05/12/2016. *Reportedly (see credibility section directly below), thirty eligible Fitbit users consented to the submission of personal tracker data, including output related to steps taken, calories burned, time spent sleeping, heart rate, and distance traveled. This data was broken down into minute, hour, and day level totals. This data is stored in 18 CSV documents. I downloaded all 18 documents into my local laptop and decided to use 2 documents for the purposes of this project as they were files which had merged activity and sleep data from the other documents. All unused documents were permanently deleted from the laptop. The 2 files used were: -sleepDaymerged.csv -dailyActivitymerged.csv Identify and communicate to stakeholders any problems found with the data related to credibility and bias. *As will be more specifically presented in the Process section, the data seems to have credibility issues related to the reported time frame of the data collected. The metadata seems to indicate that the data collected covered roughly 2 months of FitBit tracking. However, upon my initial data processing, I found that only 1 month of data was reported. *As will be more specifically presented in the Process section, the data has credibility issues related to the number of individuals who reported FitBit data. Specifically, the metadata communicates that 30 individual users agreed to report their tracking data. My initial data processing uncovered 33 individual IDs in the dailyActivity_merged dataset. *Due to the small number of participants (...

The Big Data Analytics industry in Brazil is expected to grow at a CAGR of 10.12% from 2025 to 2033, reaching a market size of USD 3.41 million by 2033. The growth is attributed to the increasing adoption of data analytics solutions by organizations across various industries, including IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare and Lifesciences, Government, and Other End-user Verticals. The rising need for real-time and actionable insights to drive informed decision-making is driving the demand for Big Data Analytics solutions, which help organizations analyze large volumes of structured and unstructured data to identify patterns, trends, and correlations. Key market trends include the adoption of cloud-based Big Data Analytics solutions, the increasing use of artificial intelligence (AI) and machine learning (ML) in data analysis, and the growing focus on data security and privacy. The competitive landscape is fragmented, with both established global players and local vendors vying for market share. Leading companies in the industry include TIBCO Software Inc, IBM Corporation, Tableu Software, WebRadar, Indicum Technology, Cotex Intelligence, SAS Institute, TAIL DMP, QlikTech, Precifica, and Splunk Inc. The market is expected to witness continued growth in the coming years, driven by the increasing adoption of Big Data Analytics solutions by organizations seeking to gain competitive advantage through data-driven insights. Recent developments include: March 2023: TIBCO Software Inc announced a series of enhancements to its analytics suite, delivering immersive, smart, and real-time analytics that empower customers to take action and benefit from faster, smarter insights. Game-changing updates to TIBCO Spotfire and other scalable analytics solutions close the gap between understanding and action, building on existing capabilities to accelerate time-to-decision and lower operations costs., December 2022: Splunk Inc. announced a five-year extension of its strategic collaboration agreement (SCA) with Amazon Web Services (AWS), Inc., where customers can create a security data lake using integrated cloud and on-premises data sources, as well as their private applications, thanks to Amazon Security Lake. With its support for the open standard Open Cybersecurity Schema Framework (OCSF), Amazon Security Lake offers customers a simpler and more cost-effective way to access data from their security solutions. This accessibility facilitates various security use cases, including threat detection, investigation, and incident response.. Key drivers for this market are: Higher Emphasis on Use of Analytics Tools to Empower Decision Making Among Large-Scale Enterprises, Rapid Increase in the Generation of Data Coupled with Availability of Several End-User Specific Tools Due to the Growth in Local Landscape; Growing Demand in Enterprise, Government and Telecom Verticals; Emerging Trends Such as Social Media Analytics to Witness the Growth. Potential restraints include: High Costs and Operational Concerns. Notable trends are: Retail & Consumer Goods is Expected to Register a Significant Growth.

Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of 8 distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T-cell morphology to transcriptional state based on their joint donor variability, and validate an inflammation-associated polarized T-cell morphology, and an age-associated loss of mitochondria in CD4+ T-cells. Taken together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping ...

Analysis of ‘911 Open Data Local Law 119’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3640bd2e-c046-4d9d-8a74-6dcb1e4467b7 on 13 November 2021.

--- Dataset description provided by original source is as follows ---

This data set provides counts and percent/average response times for the categories of incidents outlined in Local Law 119 of 2013: The Ariel Russo Emergency 9-1-1 Response Time Reporting Act.

For Local Law 119 Compliance Category Definitions please refer to this link.

--- Original source retains full ownership of the source dataset ---

Local association analysis, such as local similarity analysis and local shape analysis, of biological time series data helps elucidate the varying dynamics of biological systems. However, their applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation. We developed a theoretical approach to approximate the statistical significance of local similarity and local shape analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d) and Markovian random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points > 10 with no delay and > 20 with delay) and provides p-values comparable to those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local association analysis, making possible all-to-all association studies otherwise prohibitive. As a demonstration, local association analysis of human microbiome time series shows that core OTUs are highly synergetic and some of the associations are body-site specific across samples. The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity and shape analysis of time series data. The tool is freely available from eLSA's website: http://meta.usc.edu/softs/lsa.

This course will introduce you to two of these tools: the Hot Spot Analysis (Getis-Ord Gi*) tool and the Cluster and Outlier Analysis (Anselin Local Moran's I) tool. These tools provide you with more control over your analysis. You can also use these tools to refine your analysis so that it better meets your needs.GoalsAnalyze data using the Hot Spot Analysis (Getis-Ord Gi*) tool.Analyze data using the Cluster and Outlier Analysis (Anselin Local Moran's I) tool.

Local Government Statistics - General Statistics - Analysis of Authorized Debt and Short-Term Capital Borrowing - Regional District - 2008. The Statistics schedules consist of data provided to the ministry by local governments in annual financial reporting forms. While the ministry does perform checks of the data, we do not guarantee its accuracy or validity. Users should contact local governments directly if confirmation is required. Beginning in 2002 the schedules have been amended to reflect Generally Accepted Accounting Procedures (GAAP) for local governments, thus they differ greatly from previous years. Regional District statistics use the current year assessments supplied by BC Assessment in April and revised population estimates certified by the Minister responsible. Data for previous years may be requested electronically.

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

The Public Opinion Analysis System market is experiencing robust growth, projected to reach $3119.8 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.5% from 2025 to 2033. This expansion is driven by several key factors. Increasingly sophisticated data analytics techniques allow for deeper insights into public sentiment, enabling businesses and governments to make more informed decisions regarding marketing campaigns, policy development, and crisis management. The rise of social media and the proliferation of online platforms provide a rich source of data for analysis, fueling market demand. Furthermore, the growing adoption of cloud-based solutions offers scalability and cost-effectiveness, further accelerating market penetration. Segmentation within the market reveals a strong presence of both cloud-based and local systems, catering to diverse needs. The application spans various sectors, with significant traction in government and enterprise use cases. Competitive activity is high, with a mix of established players and emerging companies contributing to innovation and market expansion. The geographic spread is broad, with North America and Asia Pacific anticipated as leading regions, driven by technological advancements and substantial investments in data analytics. The continued growth of the Public Opinion Analysis System market is predicated on several ongoing trends. The increasing focus on data privacy and security will necessitate the development of robust and compliant solutions. Artificial intelligence (AI) and machine learning (ML) are expected to play a critical role in enhancing the accuracy and efficiency of sentiment analysis. Furthermore, the integration of public opinion analysis systems with other data sources will provide a more holistic view of public sentiment, leading to more refined insights. While challenges such as data bias and the need for skilled professionals remain, the overall market outlook remains positive, indicating substantial opportunities for both established and new entrants in the coming years.

Code used in the generation of simulated data, and analysis of the methods described in this paper.

This dataset provides all the data used to generate the Tables and Figures in the journal article titled 'Analysis of PM2.5, black carbon, and trace metals measurements from the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS)'. This dataset is associated with the following publication: Duvall, R., S. Kimbrough, S. Krabbe, P. Deshmukh, R. Baldauf, L. Brouwer, T. MacArthur, C. Croghan, J. Varga, M. Brown, and M. Davis. Analysis of PM2.5, black carbon, and trace metals measurements from the Kansas City Transportation and Local-Scale Air Quality Study. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, na, (2024).

This dataset serves as the replication material for a study originally published in the Japanese Journal of Political Science. The research encompasses three distinct analyses: Analyses 1 and 2 were conducted using Python, while Analysis 3 was performed in STATA. Both the data and the complete code for these analyses are included for reference.

This dataset provides data on a sample of 327 core cities within the EU-28, covered by the Cities Statistics database of the European Statistics Office (Eurostat), formerly known as “Urban Audit” (UA). It is organized in three spreadsheets containing, respectively, the following data: 1. List of the analyzed plans: Country / City, City population, Climate change mitigation strategy name (in national languages / in English), Year of adoption of the strategy/plan, Type of Mitigation Local Climate Plan (M-LCP), Integrated Mitigation and Adaptation Plan, Carbon neutrality, target year carbon neutrality, Global Covenant of Mayors for Climate and Energy (GCoM), Climate Alliance (CA), C40, CNCA (Carbon Neutral Cities Alliance) 2. GHG emission targets for UA cities with a plan, by country: Country / City with a plan, Type of M-LCP, CO2 emission target (% / baseline year / target year), GHG emission target (CO2eq) (% / baseline year / target year), Geographical location (Northern/Southern Europe) 3. Key data summary on the sample: Country, Total number (No.) of cities in the sample, M-LCPs by type, Total No. of M-LCP Cities without a plan, Cities with a plan, Integrated M&A LCPs, Total population, Population in our city sample, Population representativeness in the sample

Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...