標題: Titlebook: Data Cleaning; Venkatesh Ganti,Anish Das Sarma Book 2013 Springer Nature Switzerland AG 2013 [打印本頁] 作者: irritants 時間: 2025-3-21 18:41
書目名稱Data Cleaning影響因子(影響力)
書目名稱Data Cleaning影響因子(影響力)學(xué)科排名
書目名稱Data Cleaning網(wǎng)絡(luò)公開度
書目名稱Data Cleaning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Cleaning被引頻次
書目名稱Data Cleaning被引頻次學(xué)科排名
書目名稱Data Cleaning年度引用
書目名稱Data Cleaning年度引用學(xué)科排名
書目名稱Data Cleaning讀者反饋
書目名稱Data Cleaning讀者反饋學(xué)科排名
作者: 沒收 時間: 2025-3-21 21:11 作者: Formidable 時間: 2025-3-22 03:43 作者: entail 時間: 2025-3-22 06:38 作者: 古老 時間: 2025-3-22 11:26
Olaf Pollmann,Szilárd PodruzsikIn this chapter, we discuss the support that needs to be provided by a generic data cleaning platform for the task of .. As motivated in Chapter 1, the goal of deduplication is to combine records that represent the same real-world entity.作者: Locale 時間: 2025-3-22 14:48
Similarity Functions,A common requirement in several critical data cleaning operations is to measure the closeness between pairs of records. . (or, .) between atomic values constituting a record form the backbone of measuring closeness between records.作者: Locale 時間: 2025-3-22 18:46
Task: Deduplication,In this chapter, we discuss the support that needs to be provided by a generic data cleaning platform for the task of .. As motivated in Chapter 1, the goal of deduplication is to combine records that represent the same real-world entity.作者: opalescence 時間: 2025-3-22 21:27
Climate Change, Agriculture and Societyso have become the defacto standard for supporting data analysis tasks generating reports indicating the health of the business operations. These reports are often critical to track performance as well as to make informed decisions on several issues confronting a business. The reporting functionalit作者: 清唱劇 時間: 2025-3-23 02:57
Climate Change, Agriculture and Society and deployment of effective solutions for data cleaning. These approaches differ primarily in the flexibility and the effort required from the developer implementing the data cleaning solution. The more flexible approaches often require the developer to implement significant parts of the solution, 作者: 享樂主義者 時間: 2025-3-23 09:17
https://doi.org/10.1007/978-3-319-40590-2es. However, one of the crucial predicates often is to measure closeness in terms of textual context between records. This similarity is often quantified by a textual similarity function which compares the content of the two records. There are a variety of common similarity functions as discussed in作者: amygdala 時間: 2025-3-23 10:47
Energy and the Use of Nuclear Powerat the records in a group be closer to each other, especially to each other than to records in other groups. A custom deduplication task may require that other constraints beyond similarity be satisfied as well. However, closeness to each other by textual similarity is a critical predicate, which ne作者: 動脈 時間: 2025-3-23 16:28
Suraj Mal,R.B. Singh,Christian Huggel records into a target data warehouse often requires the reconciliation of schema of the input records and that of the target records. The process of reconciliation would often involve “segmenting” a column of an input record into multiple target columns. The segmented input records may then be comp作者: 冒煙 時間: 2025-3-23 21:52 作者: RACE 時間: 2025-3-24 00:29 作者: 煉油廠 時間: 2025-3-24 04:15
https://doi.org/10.1007/978-3-319-97091-2mponents of the technology. he goals of data cleaning technology in typical enterprise scenarios, as illustrated by the examples in customer and product databases, are to maintain the quality and consistency of data as the data warehouse is either being populated with data for the first time or bein作者: Exhilarate 時間: 2025-3-24 07:02 作者: Bone-Scan 時間: 2025-3-24 13:26 作者: pessimism 時間: 2025-3-24 17:12 作者: LANCE 時間: 2025-3-24 21:25
Technological Approaches, and deployment of effective solutions for data cleaning. These approaches differ primarily in the flexibility and the effort required from the developer implementing the data cleaning solution. The more flexible approaches often require the developer to implement significant parts of the solution, 作者: exclusice 時間: 2025-3-25 02:21 作者: 首創(chuàng)精神 時間: 2025-3-25 06:36
Operator: Clustering,at the records in a group be closer to each other, especially to each other than to records in other groups. A custom deduplication task may require that other constraints beyond similarity be satisfied as well. However, closeness to each other by textual similarity is a critical predicate, which ne作者: 公理 時間: 2025-3-25 09:08
Operator: Parsing, records into a target data warehouse often requires the reconciliation of schema of the input records and that of the target records. The process of reconciliation would often involve “segmenting” a column of an input record into multiple target columns. The segmented input records may then be comp作者: 要控制 時間: 2025-3-25 11:49
Task: Record Matching,esent the same real-world entity, often referred to as “matching.” This important task needs to be solved while importing new customer sales records into the customer relation in a data warehouse. The customer records in the incoming sales need to be matched with existing customers to avoid subseque作者: Nebulous 時間: 2025-3-25 16:01 作者: Proclaim 時間: 2025-3-25 23:27
Conclusion,mponents of the technology. he goals of data cleaning technology in typical enterprise scenarios, as illustrated by the examples in customer and product databases, are to maintain the quality and consistency of data as the data warehouse is either being populated with data for the first time or bein作者: 伙伴 時間: 2025-3-26 00:46 作者: Cougar 時間: 2025-3-26 04:45
Operator: Similarity Join,ied by a textual similarity function which compares the content of the two records. There are a variety of common similarity functions as discussed in the previous chapter. As in record matching, the deduplication task typically involves many predicates. However, a critical one is often based on textual similarity between records.作者: 做作 時間: 2025-3-26 08:51
Data Cleaning Scripts,erators as well as other predicates, which are required for the specific data and domain being considered. Thus, the development of custom data cleaning scripts is expected to be flexible, easy, and efficient all at the same time.作者: byline 時間: 2025-3-26 13:48 作者: BINGE 時間: 2025-3-26 20:10 作者: 事情 時間: 2025-3-26 21:31
https://doi.org/10.1007/978-3-319-97091-2g updated with fresh data subsequently. hese solutions are typically incorporated into an ETL process which is maintained in order to populate and maintain a data warehouse. A data cleaning solution is expected to address to several critical high level tasks. Some of these tasks include ., ., and ..作者: 專心 時間: 2025-3-27 02:41 作者: 羽毛長成 時間: 2025-3-27 08:55
Climate Change, Agriculture and Societyper implementing the data cleaning solution. The more flexible approaches often require the developer to implement significant parts of the solution, while the less flexible are often easier to deploy provided they meet the solution’s requirements.作者: accordance 時間: 2025-3-27 12:12
https://doi.org/10.1007/978-3-319-40590-2ied by a textual similarity function which compares the content of the two records. There are a variety of common similarity functions as discussed in the previous chapter. As in record matching, the deduplication task typically involves many predicates. However, a critical one is often based on textual similarity between records.作者: Capitulate 時間: 2025-3-27 14:06 作者: 奇怪 時間: 2025-3-27 21:50 作者: OVER 時間: 2025-3-28 01:40
Task: Record Matching,may have to be solved while deduping records (say, customers or products) in a particular relation. While record matching may be formally defined in multiple ways, below we present a commonly used abstraction:作者: 靦腆 時間: 2025-3-28 02:28
Introduction,y has become so important on its own that businesses often create consolidated data repositories. These repositories can be observed in several scenarios such as data warehousing for analysis, as well as for supporting sophisticated applications such as comparison shopping.作者: 向下 時間: 2025-3-28 08:21 作者: 非實體 時間: 2025-3-28 14:08
Conclusion,g updated with fresh data subsequently. hese solutions are typically incorporated into an ETL process which is maintained in order to populate and maintain a data warehouse. A data cleaning solution is expected to address to several critical high level tasks. Some of these tasks include ., ., and ..作者: STALE 時間: 2025-3-28 16:46
Book 2013ons. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could imp作者: Vldl379 時間: 2025-3-28 20:56
Book 2013s. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus作者: 引起痛苦 時間: 2025-3-29 02:33 作者: 鋼筆尖 時間: 2025-3-29 04:23
,Regionalismus als Abgrenzungsproze?, Untersuchungen zu diesem Thema, die sich in der einen oder anderen Hinsicht von dieser Forschungstradition l?sen. Dabei leitet sie die Vermutung, da? die sozialen Bedingungen in fortgeschrittenen bzw. sp?tmodernen Gesellschaften nicht nur katalytische, sondern kausale Wirkung auf das Erstarken regi作者: 后來 時間: 2025-3-29 08:30
