data deposition in repositories
英文版DMP模板及详细说明
英文版DMP模板及详细说明➢Instructions and footnotes in blue must not appear in the text.➢For options [in square brackets]: the option that applies must be chosen.➢For fields in [grey in square brackets] (even if they are part of an option as specified in the previous item): enter the appropriate data.Structure of the templateThe template is a set of questions that you should answer with a level of detail appropriate to the project.It is not required to provide detailed answers to all the questions in the first version of the DMP that needs to be submitted by month 6 of the project. Rather, the DMP is intended to be a living document in which information can be made available on a finer level of granularity through updates as the implementation of the project progresses and when significant changes occur. Therefore, DMPs should have a clear version number and include a timetable for updates. As a minimum, the DMP should be updated in the context of the periodic evaluation/assessment of the project. If there are no other periodic reviews envisaged within the grant agreement, an update needs to be made in time for the final review at the latest.In the following the main sections to be covered by the DMP are outlined. At the end of the document, Table 1 contains a summary of these elements in bullet form.This template itself may be updated as the policy evolves.Project1 Number: [insert project reference number]Project Acronym: [insert acronym]Project title: [insert project title]DATA MANAGEMENT PLAN1The term ‘project’ used in this template equates to an ‘action’ in certain other Horizon 2020 documentation1. Data SummaryWhat is the purpose of the data collection/generation and its relation to the objectives of the project?What types and formats of data will the project generate/collect?Will you re-use any existing data and how?What is the origin of the data?What is the expected size of the data?To whom might it be useful ('data utility')?2. FAIR data2. 1. Making data findable, including provisions for metadataAre the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? What naming conventions do you follow?Will search keywords be provided that optimize possibilities for re-use?Do you provide clear version numbers?What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how.2.2. Making data openly accessibleWhich data produced and/or used in the project will be made openly available as the default? If certain datasets cannot be shared (or need to be shared under restrictions), explain why, clearly separating legal and contractual reasons from voluntary restrictions.Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out.How will the data be made accessible (e.g. by deposition in a repository)?What methods or software tools are needed to access the data?Is documentation about the software needed to access the data included?Is it possible to include the relevant software (e.g. in open source code)?Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories which support open access where possible.Have you explored appropriate arrangements with the identified repository?If there are restrictions on use, how will access be provided?Is there a need for a data access committee?Are there well described conditions for access (i.e. a machine readable license)?How will the identity of the person accessing the data be ascertained?2.3. Making data interoperableAre the data produced in the project interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)?What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?2.4. Increase data re-use (through clarifying licences)How will the data be licensed to permit the widest re-use possible?When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible.Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why.How long is it intended that the data remains re-usable?Are data quality assurance processes described?Further to the FAIR principles, DMPs should also address:3. Allocation of resourcesWhat are the costs for making data FAIR in your project?How will these be covered? Note that costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions).Who will be responsible for data management in your project?Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)?4. Data securityWhat provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)?Is the data safely stored in certified repositories for long term preservation and curation?5. Ethical aspectsAre there any ethical or legal issues that can have an impact on data sharing? These can also be discussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapter in the Description of the Action (DoA).Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data?6. Other issuesDo you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones?7. Further support in developing your DMPThe Research Data Alliance provides a Metadata Standards Directory that can be searched for discipline-specific standards and associated tools.The EUDAT B2SHARE tool includes a built-in license wizard that facilitates the selection of an adequate license for research data.Useful listings of repositories include:Registry of Research Data RepositoriesSome repositories like Zenodo, an OpenAIRE and CERN collaboration), allow researchers to deposit both publications and data, while providing tools to link them.Other useful tools include DMP online and platforms for making individual scientific observations available such as ScienceMatters.SUMMARY TABLE 1FAIR Data Management at a glance: issues to cover in your Horizon 2020 DMPThis table provides a summary of the Data Management Plan (DMP) issues to be addressed, as outlined above.678。
data deposition in repositories -回复
data deposition in repositories -回复“data deposition in repositories”指的是将数据存放在仓库中的过程。
近年来，随着数据科学的快速发展，研究人员越来越意识到数据共享的重要性。
构建公开可访问的数据仓库既有助于数据的保存与备份，也方便了其他研究人员使用和验证数据。
本文将介绍数据存放仓库的背景、重要性以及一般的数据存放过程。
# 1. 背景数据存放仓库的兴起主要得益于以下几个方面：1.科学共享：数据是科学研究的核心，数据的共享对于快速推动科学进展至关重要。
通过数据存放仓库，研究人员可以方便地共享、沟通和合作。
2.增加可重复性：科学研究的重要性在于可重复性。
通过将数据存放在公开仓库中，其他研究人员可根据相同的数据重新分析、验证和验证现有的研究结果。
3.数据保护与备份：数据存放仓库提供了一个安全的环境来存放和备份数据。
无论是因硬盘故障、病毒入侵还是人为错误，数据仓库都保证数据的安全性。
# 2. 重要性数据存放在仓库中具有多种重要性：1.可追溯性：数据仓库要求对数据进行标准化、元数据的描述和版本控制，这样其他人可以准确地追溯研究数据的来源和处理过程。
2.开放获取：公开可访问的数据仓库使得数据资料具有更大的可访问性，任何人都可以使用这些数据进行进一步的研究。
3.快速复用：通过存放在仓库中的数据，其他研究人员可以更快速地利用现有的数据进行探索、分析和研究。
4.数据审查：数据存放仓库允许其他研究人员和领域专家对数据进行审查和验证，以确保数据的质量和可信度。
# 3. 存放过程下面是一般的数据存在仓库中的步骤：1.数据准备：在将数据存放到仓库中之前，需要对数据进行清理、标准化和格式化。
这一步骤旨在确保数据的一致性和可读性。
2.选择仓库：选择一个适合自己需求的数据存放仓库平台。
目前有许多仓库可供选择，如Zenodo、figshare、Dryad等。
3.注册账户：选择一个合适的数据存放仓库后，需要注册一个账户。
数据挖掘数据预处理DataPreprocessing2
Data Mining: Concepts and Techniques 8
Data discretization
July 13, 2013
Faulty data collection instruments Human or computer error at data entry Errors in data transmission Different data sources Functional dependency violation (e.g., modify some linked data)
July 13, 2013
Data Mining: Concepts and Techniques
6
Multi-Dimensional Measure of Data Quality
A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness Believability Value added Interpretability Accessibility Broad categories: Intrinsic, contextual, representational, and accessibility
Integration of multiple databases, data cubes, or files Normalization and aggregation
JDWM-4-3-WangSL-DataFieldsForHierarchicalClustering - paper
DOI: 10.4018/jdwm.2011100103
animation, hyperlinks, markups, and so on (Li, Zhang, & Wang, 2006; Bhatnagar, Kaur, & Mignet, 2009). Moreover, they are continuously increasing and amassed in both attribute depth and scope of instances every time. Although many decisions are made on large datasets, the huge amounts of the computerized datasets have far exceeded human ability to completely interpret (Li et al., 2006). In order to understand and make full use of these data repositories when making decisions, it is necessary to develop some technique for uncovering the physical nature inside such huge datasets.
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
44 International Journal of Data Warehousing and Mining, 7(4), 43-63, October-December 2011
数据挖掘data mining 核心专业词汇
1、Bilingual 双语Chinese English bilingual text 中英对照2、Data warehouse and Data Mining 数据仓库与数据挖掘3、classification 分类systematize classification 使分类系统化4、preprocess 预处理The theory and algorithms of automatic fingerprint identification system (AFIS) preprocess are systematically illustrated.摘要系统阐述了自动指纹识别系统预处理的理论、算法5、angle 角度6、organizations 组织central organizations 中央机关7、OLTP On-Line Transactional Processing 在线事物处理8、OLAP On-Line Analytical Processing 在线分析处理9、Incorporated 包含、包括、组成公司A corporation is an incorporated body 公司是一种组建的实体10、unique 唯一的、独特的unique technique 独特的手法11、Capabilities 功能Evaluate the capabilities of suppliers 评估供应商的能力12、features 特征13、complex 复杂的14、information consistency 信息整合15、incompatible 不兼容的16、inconsistent 不一致的Those two are temperamentally incompatible 他们两人脾气不对17、utility 利用marginal utility 边际效用18、Internal integration 内部整合19、summarizes 总结20、application-oritend 应用对象21、subject-oritend 面向主题的22、time-varient 随时间变化的23、tomb data 历史数据24、seldom 极少Advice is seldom welcome 忠言多逆耳25、previous 先前的the previous quarter 上一季26、implicit 含蓄implicit criticism 含蓄的批评27、data dredging 数据捕捞28、credit risk 信用风险29、Inventory forecasting 库存预测30、business intelligence（BI）商业智能31、cell 单元32、Data cure 数据立方体33、attribute 属性34、granular 粒状35、metadata 元数据36、independent 独立的37、prototype 原型38、overall 总体39、mature 成熟40、combination 组合41、feedback 反馈42、approach 态度43、scope 范围44、specific 特定的45、data mart 数据集市46、dependent 从属的47、motivate 刺激、激励Motivate and withstand higher working pressure个性积极，愿意承受压力.敢于克服困难48、extensive 广泛49、transaction 交易50、suit 诉讼suit pending 案件正在审理中51、isolate 孤立We decided to isolate the patients.我们决定隔离病人52、consolidation 合并So our Party really does need consolidation 所以，我们党确实存在一个整顿的问题53、throughput 吞吐量Design of a Web Site Throughput Analysis SystemWeb网站流量分析系统设计收藏指正54、Knowledge Discovery（KDD）55、non-trivial(有价值的）--Extraction interesting (non-trivial(有价值的), implicit（固有的）, previously unknown and potentially useful) patterns or knowledge from huge amounts of data.56、archeology 考古57、alternative 替代58、Statistics 统计、统计学population statistics 人口统计59、feature 特点A facial feature 面貌特征60、concise 简洁a remarkable concise report 一份非常简洁扼要的报告61、issue 发行issue price 发行价格62、heterogeneous (异类的)--Constructed by integrating multiple, heterogeneous (异类的)data sources63、multiple 多种Multiple attachments多实习64、consistent（一贯）、encode（编码）ensure consistency in naming conventions,encoding structures, attribute measures, etc.确保一致性在命名约定，编码结构，属性措施，等等。
大数据挖掘外文翻译文献
文献信息：文献标题：A Study of Data Mining with Big Data（大数据挖掘研究）国外作者：VH Shastri，V Sreeprada文献出处：《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文：大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
外文文献及翻译
What is Data Mining?Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases”, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery consists of an iterative sequence of the following steps: · data cleaning: to remove noise or irrelevant data,· data integration: where multiple data sources may be combined,· data selection : where data relevant to the analysis task are retrieved from the database,·data transformation : where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance,· data mining: an essential process where intelligent methods are applied in order to extract data patterns,·pattern evaluation: to identify the truly interesting patterns representing knowledge based on some interestingness measures, and·knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user .The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. Note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers hidden patterns for evaluation.We agree that data mining is a knowledge discovery process. However, in industry, in media, and in the database resea rch milieu, the term “data mining” is becoming more popular than the longer term of “knowledge discovery in databases”. Therefore, in this book, we choose to use the term “data mining”. We adopt a broad view of data mining functionality: data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories.Based on this view, the architecture of a typical data mining system may have the following major components:1. Database, data warehouse, or other information repository. This is one or a set of databases, data warehouses, spread sheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data.2. Database or data warehouse server. The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.3. Knowledge base. This is the domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. Knowledge such as user beliefs, which can be used to assess a pattern’s i nterestingness based on its unexpectedness, may also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources).4. Data mining engine. This is essential to the data mining system and ideally consists of a set of functional modules for tasks such as characterization, association analysis, classification, evolution and deviation analysis.5. Pattern evaluation module. This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness thresholds stored in the knowledge base. Alternatively, the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push the evaluation of pattern interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns.6. Graphical user interface. This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results. In addition, this component allows the user to browse database and data warehouse schemas or data structures, evaluate mined patterns, and visualize the patterns in different forms.From a data warehouse perspective, data mining can be viewed as an advanced stage of on-1ine analytical processing (OLAP). However, data mining goes far beyond the narrow scope of summarization-style analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding.While there may be many “data mining systems” on the market, not all of them can perform true data mining. A data analysis system that does not handle large amounts of data can at most be categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large databases should be more appropriately categorized as either a database system, an information retrieval system, or a deductive database system.Data mining involves an integration of techniques from mult1ple disciplines such as database technology, statistics, machine learning, high performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. We adopt a database perspective in our presentation of data mining in this book. That is, emphasis is placed on efficient and scalable data mining techniques for large databases. By performing data mining, interesting knowledge, regularities, or high-level information can be extracted from databases and viewed or browsed from different angles. The discovered knowledge can be applied to decision making, process control, information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems and one of the most promising, new database applications in the information industry.A classification of data mining systemsData mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and or rough set theory, knowledge representation, inductive logic programming, or high performance computing. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, Information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, or psychology.Because of the diversity of disciplines contributing to data mining, data mining research is expected to generate a large variety of data mining systems. Therefore, it is necessary to provide a clear classification of data mining systems. Such a classification may help potential users distinguish data mining systems and identifythose that best match their needs. Data mining systems can be categorized according to various criteria, as follows.1) Classification according to the kinds of databases mined.A data mining system can be classified according to the kinds of databases mined. Database systems themselves can be classified according to different criteria (such as data models, or the types of data or applications involved), each of which may require its own data mining technique. Data mining systems can therefore be classified accordingly.For instance, if classifying according to data models, we may have a relational, transactional, object-oriented, object-relational, or data warehouse mining system. If classifying according to the special types of data handled, we may have a spatial, time -series, text, or multimedia data mining system , or a World-Wide Web mining system . Other system types include heterogeneous data mining systems, and legacy data mining systems.2) Classification according to the kinds of knowledge mined.Data mining systems can be categorized according to the kinds of knowledge they mine, i.e., based on data mining functionalities, such as characterization, discrimination, association, classification, clustering, trend and evolution analysis, deviation analysis , similarity analysis, etc. A comprehensive data mining system usually provides multiple and/or integrated data mining functionalities.Moreover, data mining systems can also be distinguished based on the granularity or levels of abstraction of the knowledge mined, including generalized knowledge(at a high level of abstraction), primitive-level knowledge(at a raw data level), or knowledge at multiple levels (considering several levels of abstraction). An advanced data mining system should facilitate the discovery of knowledge at multiple levels of abstraction.3) Classification according to the kinds of techniques utilized.Data mining systems can also be categorized according to the underlying data mining techniques employed. These techniques can be described according to the degree of user interaction involved (e.g., autonomous systems, interactive exploratory systems, query-driven systems), or the methods of data analysis employed(e.g., database-oriented or data warehouse-oriented techniques, machine learning, statistics, visualization, pattern recognition, neural networks, and so on ) .A sophisticated data mining system will often adopt multiple data mining techniques or work out aneffective, integrated technique which combines the merits of a few individual approaches.什么是数据挖掘？许多人把数据挖掘视为另一个常用的术语—数据库中的知识发现或KDD的同义词。
data deposition in repositories -回复
data deposition in repositories -回复数据存储在仓库中的意义及其重要性在当今数字化时代，数据几乎无处不在。
无论是个人照片、公司财务报表还是科学研究数据，数据都扮演着至关重要的角色。
数据的存储和管理变得越来越关键，这就产生了数据仓库这一概念。
数据仓库是一个特定的存储系统，用于存储和管理各种类型和规模的数据。
它可以是一个物理设备，也可以是一个虚拟环境，但在任何情况下，数据仓库都是一个用于存储、保护和组织数据的地方。
数据仓库有着广泛的应用，包括但不限于以下几个领域：1. 企业业务数据仓库：数据仓库为企业提供了一个集中管理和组织数据的平台，从而帮助企业更好地了解其业务情况。
通过在数据仓库中存储和分析销售数据、客户数据和供应链数据等，企业能够获得对其业务运作的深入理解，并作出相应的战略决策。
2. 科学研究数据仓库：科学研究是一个数据密集型的领域，研究人员需要存储和管理大量的实验数据、研究结果等。
数据仓库可以为科学研究人员提供一个安全的存储和共享数据的环境，使得他们能够更好地进行数据分析和合作研究。
此外，数据仓库还可以帮助科学家对其研究成果进行备份和保护，以防止数据丢失。
3. 政府数据仓库：政府机构需要大量的数据来支持其政策制定和决策。
政府数据仓库是一个重要的工具，可以帮助政府机构在合规和政策制定方面进行数据分析和监测。
此外，政府数据仓库还可以提供一个共享数据的平台，促进不同机构之间的信息共享和协作。
不仅是数据存储的重要性，选择合适的数据仓库也至关重要。
以下是一些值得注意的因素：1. 数据安全性：在存储数据时，安全性是一个非常重要的考虑因素。
数据仓库需要提供可靠的安全措施，以防止未经授权的访问和数据泄露。
常见的安全措施包括访问控制、数据加密和备份策略等。
2. 数据完整性：数据完整性是指数据的准确性和一致性。
数据仓库需要确保存储的数据是完整和准确的，以便用户能够依赖这些数据进行业务分析和决策。
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data deposition in repositories
什么是数据存储库（repositories）？
数据存储库，也被称为数据仓库，是用于存储、管理和共享数据的地方。
这些数据可以是各类各样的，包括文本文档、照片、音频或视频文件，或者是结构化的数据表格。
数据存储库的目的是为了提供一个方便的方式来组织和访问数据，使其易于在不同的系统和应用程序之间共享和交换。
为什么要使用数据存储库？
数据存储库的使用有许多好处。
首先，它们提供了一个集中式的地方来存储数据，以便在需要时可以轻松地找到和检索它们。
这对于组织需要处理大量数据的公司或研究机构特别重要。
其次，数据存储库可以提供版本控制和数据管理功能，确保数据的一致性和完整性。
此外，使用存储库还可以降低数据丢失的风险，因为数据不再存储在个别计算机或设备上，而是集中在一个可靠的地方。
最重要的是，数据存储库还可以提供共享数据的机制，使研究人员、企业和其他用户能够轻松地访问和使用数据。
如何进行数据存储库的数据存储？
数据存储库的数据存储过程包括以下几步：
1. 选择合适的数据存储库：首先，您需要选择一个合适的数据存储库。
这可能涉及考虑一些因素，例如数据类型、存储需求、访问控制要求等。
一些知名的数据存储库包括GitHub、GitLab、Bitbucket等。
2. 创建仓库：一旦选择了适合的数据存储库，您需要创建一个仓库来存储和组织数据。
仓库的创建过程通常很简单，并且您可以根据需要为每个项目或数据集创建一个单独的仓库。
3. 上传数据：一旦仓库创建好了，接下来您需要将数据上传到存储库中。
这可以通过直接拖放文件到仓库中或使用存储库的命令行界面来完成。
4. 组织和管理数据：一旦数据上传到存储库中，您可以使用一些组织和管理工具来帮助您组织和管理数据。
这包括文件夹、标签、搜索功能等。
您还可以使用版本控制工具来跟踪数据的更改，以及恢复或还原以前的版本。
5. 共享数据：一旦数据存储在存储库中，您可以选择与其他人共享数据。
您可以通过提供存储库的链接、邀请其他人加入、或设置访问权限来实现共享。
这样其他人就可以轻松地访问和使用您的数据。
6. 数据访问和使用：使用存储库的用户可以根据其权限来访问和使用数据。
一些存储库可能提供公共访问权限，允许任何人都可以查看和下载数据。
另一些存储库可能设置了访问权限，只有特定用户或群组才能访问数据。
数据存储库的优势和挑战是什么？
数据存储库的优势包括：
1. 集中式管理：数据存储库提供了一个集中式的地方来存储和管理数据，使其易于查找、检索和维护。
这减少了数据丢失和混乱的风险。
2. 版本控制：使用存储库可以方便地跟踪数据的变化和更改历史，并且可以轻松地还原或恢复以前的版本。
3. 共享和协作：数据存储库使研究人员、企业和其他用户能够共享和协作使用数据。
这促进了知识共享和合作研究。
4. 数据完整性：存储库提供了一种机制来确保数据的完整性和一致性，从而提高了数据的质量和可靠性。
然而，数据存储库也面临一些挑战：
1. 存储和管理成本：存储大量数据需要相应的存储资源，并且可能会带来一定的成本。
同时，管理和维护数据存储库也需要一定的人力资源。
2. 数据安全和访问控制：数据存储库可能包含敏感或机密数据，因此需要适当的安全措施来保护数据免受未经授权的访问和滥用。
3. 数据格式和兼容性：存储库可能需要处理不同格式的数据，并且需要确保数据的兼容性和互操作性。
4. 数据冗余：存储库可能会导致数据冗余的问题，尤其是在多个存储库中存在相同或相似的数据时。
总结：
数据存储库是用于存储、管理和共享数据的地方。
通过选择合适的存储库，创建仓库，上传数据，组织和管理数据，共享数据以及管理访问权限，用户可以方便地存储、访问和共享数据。
数据存储库的使用可以提高数据管理的效率，促进数据共享和协作，并提高数据的质量和可靠性。
然而，数据存储库也面临一些挑战，如存储和管理成本、数据安全和访问控制、数据格式和兼容性等。