原博地址：https://blog.csdn.net/lilanfeng1991/article/details/434515911.http://projects.iq.harvard.edu/cces/homeOpinion evolution model of social network based on information entropy 科学网https://www.researchgate.net/
美国马里兰大学http://glcf.umiacs.umd.edu/data/landsat/
地理数据 在资源生态环境方面的应用或研究，数据源可选择Landsat、资源卫星-2A（B）、环境小卫星数据、Modis数据、NOAA的AVHHR、SRTM的地形数据，这些数据基本可以免费获取。中国资源卫星数据服务网http://www.cresda.com说明：需要注册一个账户，信息要真实，他会经过一定程序的验证，当身份通过之后，你就可以下载上面的数据了。还有一种方式是通过单位开据证明信，可以传真给中国资源卫星应用中心相关人员，说明使用目的，他们会以光盘形式发放。环保部环境星下载服务网www.secmep.cn说明：需要注册一个账户，以及提交相关信息。经过验证后给予开通，可下载环境小卫星影像数据。风云卫星遥感数据服务网http://fy3.satellite.cma.gov.cn/PortalSite/default.aspx说明：上面集合了风云系列卫星、NOAA卫星数据下载。对地观测数据共享服务网共享了中分辨率卫星遥感数据，国内用户通过可视化的地图窗口能够查询检索并下载共享数据。LANDSAT-5、LANDSAT-7、RESOURCESAT-1、ERS-2、ENVISAT等中等分辨率的卫星遥感数据向全国开放，首批共享数据将达到2.3万景。注册时信息尽量详细，会通过电话验证。网址： http://ids.ceode.ac.cn。数字黑河共享网http://heihe.westgis.ac.cn/说明：原则上两个网站都需要书面申请．但可以通过Ｅ-mail联系他们，说明特殊情况，他们会尽快给你开通ＦＴＰ帐户和密码及端口等．地球系统科学数字共享网一般３个工作日内有回复，数字黑河可能更快。国际数据服务平台http://datamirror.csdb.cn/数据资源包括Modis、Landsat、SRTM、ASTER GDEM、NCAR及天然气水合物的部分数据库，并与国际数据资源保持同步更新。ENVISAT-ASAR雷达卫星数据共享http://ds.rsgs.ac.cn说明：只需要注册一个账号，大约经过2星期就能通过审查。包括了大量的ENVISAT-ASAR的存档数据下载，速度挺快。http://datashare.rsgs.ac.cnMODIS数据国内下载网1、中国农科院资源区划所MODIS地面接收站http://www.modis.net.cn/2、中科院地理所Modis共享网http://www.nfiieos.cn/说明：国内还有多家Modis接收站，不过只有这两个网站下载系统经常能访问。MODIS数据国外下载网美国NASA的MODIS下载网http://modis.gsfc.nasa.gov/http://ladsweb.nascom.nasa.gov/data/说明：比较全，更新速度非常快。能下载原始MODIS数据。Landsat数据下载美国马里兰大学http://glcf.umiacs.umd.edu/data/landsat/说明：上面有很多免费下载数据的网站链接。http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsphttp://earthexplorer.usgs.gov/说明：landsat7数据以及其他数据全球SRTM地形数据下载网NASA网站公布的下载地址。ftp://e0mss21u.ecs.nasa.gov/srtm/http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp说明：这个FTP经常关。全球基于Aster的DEM数据下载网站https://wist.echo.nasa.gov说明：这个网站还可以下载其他数据，包括Landsat、Aster、Modis产品数据等。Hyperion数据下载http://glovis.usgs.gov/全球各国shape数据下载，包括矢量要素、dem数据、遥感图片，免费，精度不知。http://biogeo.berkeley.edu/bgm/gdata.php6.电影评论http://grouplens.org/datasets/movielens/机器学习中推荐算法数据集http://archive.ics.uci.edu/ml/8.开放的数据http://mp.weixin.qq.com/s?__biz=MjM5MTQzNzU2NA==&mid=401644978&idx=1&sn=63b412e0e9c8fd46a0447381bda2fbf0&scene=1&srcid=01053kcLazU0kjdSY4qZ3Pyk&from=singlemessage&isappinstalled=0#wechat_redirect(1)European Data Portalhttp://open-data.europa.eu/en/data/信息量大：囊括了来自34个国家、总计达24万的数据集。分类清晰：数据分为从农业到交通等13个类别，包括科学、司法、卫生，这能让你按照分类浏览。(2)Open Data Inception Projecthttp://www.bespacific.com/open-data-inception-1600-open-data-portals-around-the-world/整合全球1600个公开数据门户的数据目录正是你的好选择。例如数据平台OpenGeocode（免费公共地理位置数据库），Dataportals（公开数据门户），问答门户Quora和StackExchange等都被统统囊括其中。OpenDataSoft平台可以让用户将不同资源添加到同一个数据集。因此，他们添加了所有收集的数据以及能连接到线上表格的链接，这样就能在表格中手动添加数据，同时让数据和主数据集同步。整合的所有门户当中，深度君特别推荐整合了全球519个数据门户的超全数据清单Data Portals，其alpha版本已在2011年开放知识基金会伦敦大会上发布。用户可以在搜索框里键入任意关键词，例如键入“China”一词，下方地图即会显示数量和所在地点，点击标注点，可看信息网址和简介。目前列表中有5个中国的数据门户，分别为北京市政务数据资源网、大连市政府信息公开网、中国政府公开信息整合服务平台和上海市政府数据服务网。（3）世行报告http://www.doingbusiness.org/data世界银行在年末出版的《2016营商环境报告：测评监管质量与效率》（Doing Business 2016: Measuring Regulatory Quality and Efficiency）属首选之列。该报告是世界银行系列年度报告之一，用于评估加强或限制商业活动的条例。涵盖了从阿富汗到津巴布韦在内的189个经济体可比较的商业条例和知识产权保护的衡量标准。（4）联合国千年发展目标报告UN MDG Reportshttp://www.un.org/en/development/desa/policy/mdg_gap/mdg_gap_archive.shtml（5）联合国数据（UN Data）（6）CloudConvert：211种文件格式随你转换作者：feng_lilan来源：CSDN原文：https://blog.csdn.net/lilanfeng1991/article/details/43451591版权声明：本文为博主原创文章，转载请附上博文链接！}

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音频文件在下方，点击获取。11. Differentiate between data warehouse and database.Database: A database is a logically organized collection of structured data kept electronically in a computer system. A database management system is usually in charge of a database (DBMS). The data, the DBMS, and the applications that go with them are referred to as a database system, which is commonly abbreviated to just a database.The following table enlists the difference between data warehouse and database:Data WarehouseDatabaseData Warehouse uses the OnLine Analytical Processing (OLAP).Database uses the OnLine Transactional Processing (OLTP).Data Warehouse is mainly used for analyzing the historical data so as to make future decisions based on them.The database aids in the execution of basic business procedures.Because a data warehouse is denormalized, tables and joins are straightforward.A database's tables and joins are complicated because they are normalised.It can be referred to as a subject-oriented collection of data.It can be referred to as an application-oriented collection of data.In this, data modelling techniques are used for designing.In this, Entity-Relationship (ER) modelling techniques are used for designing.Data may not be up to date in this.Data is generally up to date in this.The data structure of Data Warehouse is based on a dimensional and normalised approach. For example, a star and snowflake schema is employed.For data storing, the Flat Relational Approach approach is employed.Generally, highly summarized data is stored in a data warehouse.Generally, detailed data is stored in a database.11.区分数据仓库和数据库。数据库：数据库是以电子方式保存在计算机系统中的结构化数据的逻辑组织集合。数据库管理系统通常负责数据库 (DBMS)。数据、DBMS 和与之配套的应用程序被称为数据库系统，通常简称为数据库。下表列出了数据仓库和数据库之间的区别：数据仓库数据库数据仓库使用在线分析处理 (OLAP)。数据库使用联机事务处理 (OLTP)。数据仓库主要用于分析历史数据，以便根据这些数据做出未来的决策。该数据库有助于执行基本的业务程序。因为数据仓库是非规范化的，所以表和连接很简单。数据库的表和连接很复杂，因为它们是规范化的。它可以被称为面向主题的数据集合。它可以被称为面向应用程序的数据集合。在此，数据建模技术用于设计。在此，实体关系（ER）建模技术用于设计。此处的数据可能不是最新的。数据通常是最新的。数据仓库的数据结构基于维度和规范化的方法。例如，采用星形和雪花模式。对于数据存储，采用平面关系方法方法。通常，高度汇总的数据存储在数据仓库中。通常，详细数据存储在数据库中。12. What do you mean by a factless fact table in the context of data warehousing?A fact table with no measures is known as a factless fact table. It's essentially a crossroads of dimensions (it contains nothing but dimensional keys). One form of factless table is used to capture an event, while the other is used to describe conditions.In the first type of factless fact table, there is no measured value for an event, but it develops the relationship among the dimension members from several dimensions. The existence of the relationship is itself the fact. This type of fact table can be utilised to create valuable reports on its own. Various criteria can be used to count the number of occurrences.The second type of factless fact table is a tool that's used to back up negative analytical reports. Consider a store that did not sell a product for a period of time. To create such a report, you'll need a factless fact table that captures all of the conceivable product combinations that were on offer. By comparing the factless table to the sales table for the list of things that did sell, you can figure out what's missing.12. 在数据仓库的上下文中，无事实事实表是什么意思？没有度量的事实表称为无事实事实表。它本质上是维度的十字路口（它只包含维度键）。一种形式的无事实表用于捕获事件，而另一种形式用于描述条件。在第一类无事实事实表中，事件没有度量值，但它从多个维度发展了维度成员之间的关系。关系的存在本身就是事实。这种类型的事实表可用于自行创建有价值的报告。可以使用各种标准来计算出现次数。第二种类型的无事实事实表是一种用于备份负面分析报告的工具。考虑一家在一段时间内没有销售产品的商店。要创建这样的报告，您需要一个无事实的事实表，该表可以捕获所有可能提供的产品组合。通过将无事实表与销售表进行比较以获取已售出的商品列表，您可以找出缺少的内容。13. What do you mean by Real time data warehousing?A system that reflects the condition of the warehouse in real time is referred to as real-time data warehousing. If you perform a query on the real-time data warehouse to learn more about a specific aspect of the company or entity described by the warehouse, the result reflects the status of that entity at the time the query was run. Most data warehouses contain data that is highly latent — that is, data that reflects the business at a specific point in time. A real-time data warehouse provides current (or real-time) data with low latency.13. 实时数据仓库是什么意思？实时反映仓库状况的系统称为实时数据仓库。如果您对实时数据仓库执行查询以了解有关仓库描述的公司或实体的特定方面的更多信息，则结果会反映该实体在运行查询时的状态。大多数数据仓库包含高度潜在的数据，即反映特定时间点业务的数据。实时数据仓库以低延迟提供当前（或实时）数据。14. What do you mean by Active Data Warehousing?The technical capacity to collect transactions as they change and integrate them into the warehouse, as well as maintaining batch or planned cycle refreshes, is known as active data warehousing. Automating routine processes and choices is possible with an active data warehouse. The active data warehouse sends decisions to the On-Line Transaction Processing (OLTP) systems automatically. An active data warehouse is designed to capture and distribute data in real time. They give you a unified view of your customers across all of your business lines. Business Intelligence Systems are linked to it.14. 动态数据仓库是什么意思？在交易发生变化时收集交易并将其集成到仓库中以及维护批量或计划的周期更新的技术能力被称为动态数据仓库。使用动态数据仓库可以自动化日常流程和选择。动态数据仓库自动将决策发送到在线事务处理 (OLTP) 系统。动态数据仓库旨在实时捕获和分发数据。它们为您提供所有业务线中客户的统一视图。商业智能系统与它相关联。15. What are the characteristics of a data warehouse?Following are the characteristics of a data warehouse:15、数据仓库的特点是什么？以下是数据仓库的特征：Subject-oriented : Because it distributes information about a theme rather than an organization's actual operations, a data warehouse is always subject-oriented. It is possible to do so with a certain theme. That is to say, the data warehousing procedure is intended to deal with a more defined theme. These themes could include sales, distribution, and marketing, for example. The focus of a data warehouse is never solely on present activities. Instead, it concentrates on demonstrating and analyzing evidence in order to reach diverse conclusions. It also provides a simple and precise demonstration around a specific theme by removing info that isn't needed to make conclusions.Integrated : It is similar to subject orientation in that it is created in a dependable format. Integration entails the creation of a single entity to scale all related data from several databases. The data has to be stored in several data warehouses in a shared and widely accessible manner. A data warehouse is created by combining information from a variety of sources, such as a mainframe and a relational database. It must also have dependable naming conventions, formats, and codes. The utilization of a data warehouse allows for more effective data analysis. The consistency of name conventions, column scaling, and encoding structure, among other things, should be validated. The data warehouse integration handles a variety of subject-related warehouses.Time-Variant : Data is kept in this system at various time intervals, such as weekly, monthly, or annually. It discovers a number of time limits that are structured between massive datasets and held in the online transaction process (OLTP). Data warehouse time limitations are more flexible than those of operational systems. The data in the data warehouse is predictable over a set period of time and provides information from a historical standpoint. It contains explicit or implicit time elements. Another property of time-variance is that data cannot be edited, altered, or updated once it has been placed in the data warehouse.Non-volatile : The data in a data warehouse is permanent, as the name implies. It also means that when new data is put, it is not erased or removed. It incorporates a massive amount of data that is placed into logical business alteration between the designated quantity. It assesses the analysis in the context of warehousing technologies. Data is read-only and refreshed at scheduled intervals. This is useful for analyzing historical data and understanding how things work. It is not required to have a transaction process, a recapture mechanism, or a concurrency control mechanism. In a data warehouse environment, operations like delete, update, and insert that are performed in an operational application are lost.面向主题：因为它分发有关主题的信息而不是组织的实际操作，所以数据仓库始终是面向主题的。可以使用某个主题来执行此操作。也就是说，数据仓库过程旨在处理更明确的主题。例如，这些主题可能包括销售、分销和营销。数据仓库的重点绝不仅仅是当前的活动。相反，它专注于展示和分析证据以得出不同的结论。它还通过删除不需要得出结论的信息来围绕特定主题提供简单而精确的演示。集成：它类似于主题方向，因为它是以可靠的格式创建的。集成需要创建一个实体来扩展来自多个数据库的所有相关数据。数据必须以共享且可广泛访问的方式存储在多个数据仓库中。数据仓库是通过组合来自各种来源（例如大型机和关系数据库）的信息而创建的。它还必须具有可靠的命名约定、格式和代码。数据仓库的使用允许更有效的数据分析。应验证名称约定、列缩放和编码结构等的一致性。数据仓库集成处理各种与主题相关的仓库。时变：数据以不同的时间间隔保存在该系统中，例如每周、每月或每年。它发现了许多时间限制，这些时间限制在海量数据集之间构建并保存在在线交易流程 (OLTP) 中。数据仓库的时间限制比操作系统更灵活。数据仓库中的数据在设定的时间段内是可预测的，并从历史的角度提供信息。它包含显式或隐式时间元素。时变的另一个特性是数据一旦被放入数据仓库就不能被编辑、更改或更新。非易失性：顾名思义，数据仓库中的数据是永久性的。这也意味着当放入新数据时，它不会被擦除或删除。它包含大量数据，这些数据被放置在指定数量之间的逻辑业务变化中。它在仓储技术的背景下评估分析。数据是只读的，并按计划的时间间隔刷新。这对于分析历史数据和了解事物的运作方式很有用。它不需要有事务处理、重新捕获机制或并发控制机制。在数据仓库环境中，在操作应用程序中执行的删除、更新和插入等操作会丢失。16. What do you understand about metadata and why is it used for?Metadata is defined as information about data. Metadata is the context that provides data a more complete identity and serves as the foundation for its interactions with other data. It can also be a useful tool for saving time, staying organised, and getting the most out of the files you're working with. Structural Metadata describes how an object should be classified in order to fit into a wider system of things. Structural Metadata makes a link with other files that allows them to be categorized and used in a variety of ways. Administrative Metadata contains information about an object's history, who owned it previously, and what it can be used for. Rights, licences, and permissions are examples. This information is useful for persons who are in charge of managing and caring for an asset.When a piece of information is placed in the correct context, it takes on a whole new meaning. Furthermore, better-organized Metadata will considerably reduce search time.元数据被定义为关于数据的信息。元数据是为数据提供更完整身份并作为其与其他数据交互的基础的上下文。它也可以是一个有用的工具，可以节省时间、保持井井有条并充分利用您正在处理的文件。结构元数据描述了一个对象应该如何分类以适应更广泛的事物系统。结构元数据与其他文件建立链接，从而可以对它们进行分类和以多种方式使用。管理元数据包含有关对象历史、之前拥有它的人以及它可以用于什么的信息。权利、许可和许可是示例。此信息对于负责管理和照管资产的人员很有用。当一条信息被放置在正确的上下文中时，它就会具有全新的含义。此外，组织更好的元数据将大大减少搜索时间。17. Explain what you mean by a star schema in the context of data warehousing.Star schema is a sort of multidimensional model and is used in a data warehouse. The fact tables and dimension tables are both contained in the star schema. There are fewer foreign-key joins in this design. With fact and dimension tables, this schema forms a star.17. 解释在数据仓库环境中星型模式的含义。星型模式是一种多维模型，用于数据仓库。事实表和维度表都包含在星型模式中。此设计中的外键连接较少。对于事实表和维度表，此模式形成一个星形。18. What do you mean by snowflake schema in the context of data warehousing?Snowflake Schema is a multidimensional model that is also used in data warehouses. The fact tables, dimension tables, and sub dimension tables are all contained in the snowflake schema. With fact tables, dimension tables, and sub-dimension tables, this schema forms a snowflake.18. 在数据仓库的上下文中，雪花模型是什么意思？雪花模型是一种多维模型，也用于数据仓库。事实表、维度表和子维度表都包含在雪花模式中。对于事实表、维度表和子维度表，此模式形成了一片雪花。}