Reducing duplicated data of database backups is an important application scenario for data deduplication technology. NewSQL is an emerging database system and is now being used more and more widely. NewSQL systems need to improve data reliability by periodically backing up in-memory data, resulting in a lot of duplicated data. The traditional deduplication method is not optimized for the NewSQL server system and cannot take full advantage of hardware resources to optimize deduplication performance. A recent research pointed out that the future NewSQL server will have thousands of CPU cores, large DRAM and huge NVRAM. Therefore, how to utilize these hardware resources to optimize the performance of data deduplication is an important issue. To solve this problem, we propose a deduplication optimization method (DOMe) for NewSQL system backup. To take advantage of the large number of CPU cores in the NewSQL server to optimize deduplication performance, DOMe parallelizes the deduplication method based on the fork-join framework. The fingerprint index, which is the key data structure in the deduplication process, is implemented as pure in-memory hash table, which makes full use of the large DRAM in NewSQL system, eliminating the performance bottleneck problem of fingerprint index existing in traditional deduplication method. The H-store is used as a typical NewSQL database system to implement DOMe method. DOMe is experimentally analyzed by two representative backup data. The experimental results show that: 1) DOMe can reduce the duplicated NewSQL backup data. 2) DOMe significantly improves deduplication performance by parallelizing CDC algorithms. In the case of the theoretical speedup ratio of the server is 20.8, the speedup ratio of DOMe can achieve up to 18; 3) DOMe improved the deduplication throughput by 1.5 times through the pure in-memory index optimization method.

Environics Analytics provides you with comprehensive set of demographic data, including two versions of the Canadian Census: Statistics Canada’s official release and our CensusPlus, which reconciles the effects of random rounding and suppression by the government. These authoritative data help you make informed decisions on how to reach your best customers, track neighbourhood growth patterns and forecast trends with census projections.how it's used:Marketers can use CensusPlus’ demographic data to analyze custom trade areas to help locate attractive target markets for more effective direct mail campaigns.Retailers can enhance their customer databases with demographic profiles to better understand their best customers. CensusPlus’ multicultural data can also help users analyze their culturally diverse customers so users can make more informed decisions on how to meet their needs.Creating target sets by grouping together demographically similar customer segments can help marketers develop meaningful messaging that is more likely to resonate with current and prospective customers.

Abstract

Mauritania, like many countries in the Sahel, regularly face recurrent plagues such as droughts, floods, bird invasions, off-season rains, as well as, regional security issues. Drought, for example, is a common phenomenon in the south of Mauritania, which favors food insecurity and malnutrition, and significantly reduces household resilience while increasing their vulnerability to future shocks. Apart from the fact that only 0.5 percent of the land is suitable for agriculture, Mauritania consists of reliefs and very large, fragile agroecological complexes which are also faced with the effects of climate change.

In recent years, food crises and nutritional factors have been regularly observed due to structural causes which has poverty as its common denominator. These crises, as well as, climatic factors have a negative consequence on natural resources and reduce the resilience of livelihoods, thereby generating a loss in productivity and poor governance of natural resources. The concept of resilience generally defines the capacity of individuals, households, communities and countries to absorb shocks and adapt to a changing environment, while transforming the institutional environment in the long term. Thus, it is necessary to set up interventions that will have an impact on adaptability and risk management over time, in order to strengthen the resilience of vulnerable households.

For more than 10 years, FAO has measured the household resilience index in different countries, using a tool developed for this purpose; Resilience Index Measure and Analysis (RIMA). RIMA analysis requires household data, covering the different aspects of livelihood; activities (productive and non-productive), social safety nets, income, access to basic services (such as schools, markets, transportation etc.) and adaptive capacity. Following the two RIMA surveys carried out in 2015 and 2016 during the lean season and the post-harvest period, this survey was carried out in 2017 to determine the resilience index in all regions of the country.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling size used in the household survey was determined by the FAO - ESA statistical team based on the results of the General Census of Population and Housing (RGPH) 2013, Permanent Survey on Household Living Conditions (EPCV) 2014 and the results of Resilience Index Measurement and Analysis (RIMA) surveys conducted in 2015 and 2016. A total sample of 3,560 households was selected. A 2 stage, simple random sampling method was employed to select the sampled households, distributed among the different rural and urban areas of the country.

The first stage sampling frame consists of an exhaustive list of Census Districts (CD) from the mapping of the RGPH carried out in 2013. An average CD has a population of about 1,000 people (approximately 200 households). The frame has been reorganized into 25 strata, corresponding to the total number of districts in the country, each subdivided into two environments, except Nouakchott which constitutes the 25th stratum. Drawing units called primary units are made up of census districts in the sampling frame at the level of each stratum.

The second stage sampling frame consists of the list of households in each CD sampled. This database was updated after a preliminary count which takes place shortly before the actual data collection in order to reduce the risks linked to the mobility of households. A total of 20 households were drawn from each CD counted.

Out of the 3,560 sampled households, 2,826 were interviewed.

Sampling deviation

Some teams encountered several difficulties related mainly to access, due to the collection period (winter). Also, the methodology used i.e carrying out census of districts before drawing sample households caused a delay in data collection and therefore the time provided was not sufficient to ensure collection at all level of the sampled census districts.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data collection operation was performed using tablets. The program entered, designed by the statistical office has been tested and all constraints/controls necessary to ensure data quality have been integrated into the program.This program has been shared and tested before training. Also, consistency procedures have been incorporated into the program to minimize collection errors and ensure harmonization and consistency between different sections of the questionnaire.

In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Response rate

The response rate was 79.4%.

Data appraisal

A 5-day training was provided by the FAO team in collaboration with the team from the national statistical office on the RIMA-national questionnaire. This training was done to examine the questionnaire and explain to the different participants the meaning of all the questions asked. During this training, a practical session on the tablets was provided by the statistical team in order to allow the data collection agents understand the handling and testing of the questionnaire. At the end of this training, a pilot survey was organized in some districts of Nouakchott. This survey revealed errors in the collection program which were corrected before field teams were deployed for data collection.

The data collection in the field lasted 1 month and 10 days. In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

ConceptNet5

Galor and Özak (2016) introduced a novel index of land suitability: “The Caloric Suitability Indices” (CSI) that capture the variation in potential crop yield across the globe, as measured in calories per hectare per year. Moreover, in light of the expansion in the set of crops available for cultivation in the course of the Columbian Exchange, the CSI indices provide a distinct measure for caloric suitability for the pre-1500 and the post-1500 era.

The CSI indices provide four estimates of caloric suitability for each cell of size 5′× 5 in the world:

The maximum potential caloric yield that is attainable given the set of crops suitable for cultivation in the pre-1500 period.

The maximum potential caloric yield that is attainable given the set of crops suitable for cultivation in the post-1500 period.

The average potential yield (within each cell) attainable given the set of crops suitable for cultivation in the pre-1500 period.

The average potential yield (within each cell) attainable given the set of crops suitable for cultivation in the post-1500 period.

This Zenodo repository serves as a backup and public source for the data. See the Caloric Suitability Index website for more information.

Comparison of our SSD and Intel Optane.

Index to the BGS collection of downhole CCTV recordings and also backup tapes for SKYLAB satellite imagery. The Oracle index was setup in 1997 and covers the whole of Great Britain, all received videos are indexed but the level of detail in the index may vary between entries.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Commvault Systems (CVLT) Stock Forecast: Get Ready for a Data Backup Bonanza!

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This is a vector line showing the designated trail alignment of the Old Spanish National Historic Trail. The coordinates for this dataset were gathered from a variety of sources at a variety of scales. Some source materials were large-scale, while others were small-scale and included 1:100,000 or even 1:500,000 material. The purpose of this feature service is to show the location of designated trail alignment of the Old Spanish National Historic Trail. The intended use of all data in the trail GIS library is to support diverse trail administration activities including planning, management, maintenance, research, and interpretation. Old Spanish NHT congressionally designated alignment as depicted in the Comprehensive Administrative Strategy.To view additional metadata and to download the shapefile, please visit the National Park Service, Integrated Resource Management Application (IRMA) website: https://irma.nps.gov/DataStore/Reference/Profile/2244633

This is a backup of all Inventory and Monitoring Network discrete water quality data uploaded to the EPA Water Quality Exchange/ STORET as of May 2025.

This is a backup from March 1, 2025 of all public documents available from

1. The name of the file for each instance consists of 9 numbers separated by hyphens. The meaning of each number is summarized as follows: a) 1st number: number of depots b) 2nd number: number of handover points c) 3rd number: total number of night-shift duties d) 4th number: number of night-shift duties whose crews are backups or arrive at a depot by deadheading e) 5th number: number of night-shift duties whose crews drive the train to arrive at a depot f) 6th number: total number of morning-shift duties g) 7th number: number of morning-shift duties whose crews are backups or depart from a depot by deadheading h) 8th number: number of morning-shift duties whose crews drive the train to depart from a depot i) 9th number: index of the instance in the subset
2. In our instances, ρ=1000000, M_1=97200, M_2=46800.
3. In real-world instances, index of depots 1,2,3, and 4 represents Tianhe Depot, Fozuling Depot, Changqing Depot, and Zhongshan North Road Depot, respectively.

Results of the global Moran index of urban development in Jilin province and its significance test.

The 30 m / month vegetation index (NDVI / EVI) data set of Heihe River basin provides the monthly NDVI / EVI composite products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set. The average composite MC method is used as the main algorithm for synthesis, and the backup algorithm uses VI method. At the same time, the main observation angles of the multi-source data set are used as part of the quality descriptor to help analyze the angle effect of the composite vegetation index residue. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use the multi-temporal and multi angle observation data, before using the multi-source data set to synthesize the vegetation index, the algorithm designs the data quality inspection of the multi-source data set, removing the observation with large error and inconsistent observation. The verification results in the middle reaches of Heihe River show that the NDVI / EVI composite results of the combined multi temporal and multi angle observation data are in good agreement with the ground measured data (R2 = 0.89, RMSE = 0.092). In a word, the 30 m / month NDVI / EVI data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to realize the stable standardized products from scratch and better serve the application of remote sensing data products.

Transforming resource-based cities into sustainable economic development is a great challenge for policy-makers in many countries. However, the economic-centered evaluation system tends to breed the undesirable view of "GDP only" or “brown growth” in the previous case studies which is inconsistent with the long-run and sustainable development of resource-based cities. To fill in this research gap, this paper takes Jilin province in northeast China as a case study to explore urban development problems faced by major resource-based cities during resource depletion. This research constructs a stratified indicator system and conducts an in-depth analysis of the features and spatial effects of urban decline. For this analysis, this paper jointly uses the methods of entropy-weighted TOPSIS, analytic hierarchical process (AHP), and spatial effect model based on a database from 2000 to 2019. The findings of this study show that the current transformation of resource-based cities in Jilin province is generally ineffective and difficult to maintain long-run and sustainable development due to its historical reasons and industrial development background. According to the results, the resource-based cities in Jilin province show an unstable development because of factors such as barriers to the physical renewal of resources, rigid industrial structure, insufficient backup resources, and institutional and policy constraints. Also, the transformation of these cities into sustainable economic development is still facing demographic, social, and ecological difficulties.

Transforming resource-based cities into sustainable economic development is a great challenge for policy-makers in many countries. However, the economic-centered evaluation system tends to breed the undesirable view of "GDP only" or “brown growth” in the previous case studies which is inconsistent with the long-run and sustainable development of resource-based cities. To fill in this research gap, this paper takes Jilin province in northeast China as a case study to explore urban development problems faced by major resource-based cities during resource depletion. This research constructs a stratified indicator system and conducts an in-depth analysis of the features and spatial effects of urban decline. For this analysis, this paper jointly uses the methods of entropy-weighted TOPSIS, analytic hierarchical process (AHP), and spatial effect model based on a database from 2000 to 2019. The findings of this study show that the current transformation of resource-based cities in Jilin province is generally ineffective and difficult to maintain long-run and sustainable development due to its historical reasons and industrial development background. According to the results, the resource-based cities in Jilin province show an unstable development because of factors such as barriers to the physical renewal of resources, rigid industrial structure, insufficient backup resources, and institutional and policy constraints. Also, the transformation of these cities into sustainable economic development is still facing demographic, social, and ecological difficulties.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.