پاورپوینت کامل Chapter 18: Data Analysis and Mining 59 اسلاید در PowerPoint

توجه : این فایل به صورت فایل power point (پاور پوینت) ارائه میگردد

 پاورپوینت کامل Chapter 18: Data Analysis and Mining 59 اسلاید در PowerPoint دارای ۵۹ اسلاید می باشد و دارای تنظیمات کامل در PowerPoint می باشد و آماده ارائه یا چاپ است

شما با استفاده ازاین پاورپوینت میتوانید یک ارائه بسیارعالی و با شکوهی داشته باشید و همه حاضرین با اشتیاق به مطالب شما گوش خواهند داد.

لطفا نگران مطالب داخل پاورپوینت نباشید، مطالب داخل اسلاید ها بسیار ساده و قابل درک برای شما می باشد، ما عالی بودن این فایل رو تضمین می کنیم.

توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل می باشد و در فایل اصلی پاورپوینت کامل Chapter 18: Data Analysis and Mining 59 اسلاید در PowerPoint،به هیچ وجه بهم ریختگی وجود ندارد

بخشی از مطالب داخلی اسلاید ها

پاورپوینت کامل Chapter 18: Data Analysis and Mining 59 اسلاید در PowerPoint

اسلاید ۴: Decision-Support Systems: OverviewData analysis tasks are simplified by specialized tools and SQL extensionsExample tasksFor each product category and each region, what were the total sales in the last quarter and how do they compare with the same quarter last yearAs above, for each product category and each customer categoryStatistical analysis packages (e.g., : S++) can be interfaced with databasesStatistical analysis is a large field, but not covered hereData mining seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases.A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site.Important for large businesses that generate data from multiple divisions, possibly at multiple sitesData may also be purchased externally

اسلاید ۵: Data Analysis and OLAPOnline Analytical Processing (OLAP)Interactive analysis of data, allowing data to be summarized and viewed in different ways in an online fashion (with negligible delay)Data that can be modeled as dimension attributes and measure attributes are called multidimensional data.Measure attributes measure some valuecan be aggregated upone.g. the attribute number of the sales relationDimension attributesdefine the dimensions on which measure attributes (or aggregates thereof) are viewede.g. the attributes item_name, color, and size of the sales relation

اسلاید ۶: Cross Tabulation of sales by item-name and colorThe table above is an example of a cross-tabulation (cross-tab), also referred to as a pivot-table.Values for one of the dimension attributes form the row headersValues for another dimension attribute form the column headersOther dimension attributes are listed on topValues in individual cells are (aggregates of) the values of the dimension attributes that specify the cell.

اسلاید ۷: Relational Representation of Cross-tabsCross-tabs can be represented as relationsWe use the value all is used to represent aggregatesThe SQL:1999 standard actually uses null values in place of all despite confusion with regular null values

اسلاید ۸: Data CubeA data cube is a multidimensional generalization of a cross-tabCan have n dimensions; we show 3 below Cross-tabs can be used as views on a data cube

اسلاید ۹: Online Analytical ProcessingPivoting: changing the dimensions used in a cross-tab is called Slicing: creating a cross-tab for fixed values onlySometimes called dicing, particularly when values for multiple dimensions are fixed.Rollup: moving from finer-granularity data to a coarser granularity Drill down: The opposite operation – that of moving from coarser-granularity data to finer-granularity data

اسلاید ۱۰: Hierarchies on DimensionsHierarchy on dimension attributes: lets dimensions to be viewed at different levels of detailE.g. the dimension DateTime can be used to aggregate by hour of day, date, day of week, month, quarter or year

اسلاید ۱۱: Cross Tabulation With HierarchyCross-tabs can be easily extended to deal with hierarchiesCan drill down or roll up on a hierarchy

اسلاید ۱۲: OLAP ImplementationThe earliest OLAP systems used multidimensional arrays in memory to store data cubes, and are referred to as multidimensional OLAP (MOLAP) systems.OLAP implementations using only relational database features are called relational OLAP (ROLAP) systemsHybrid systems, which store some summaries in memory and store the base data and other summaries in a relational database, are called hybrid OLAP (HOLAP) systems.

اسلاید ۱۳: OLAP Implementation (Cont.)Early OLAP systems precomputed all possible aggregates in order to provide online responseSpace and time requirements for doing so can be very high2n combinations of group byIt suffices to precompute some aggregates, and compute others on demand from one of the precomputed aggregatesCan compute aggregate on (item-name, color) from an aggregate on (item-name, color, size) For all but a few “non-decomposable” aggregates such as medianis cheaper than computing it from scratch Several optimizations available for computing multiple aggregatesCan compute aggregate on (item-name, color) from an aggregate on (item-name, color, size)Can compute aggregates on (item-name, color, size), (item-name, color) and (item-name) using a single sorting of the base data

اسلاید ۱۴: Extended Aggregation in SQL:1999The cube operation computes union of group by’s on every subset of the specified attributesE.g. consider the queryselect item-name, color, size, sum(number) from sales group by cube(item-name, color, size) This computes the union of eight different groupings of the sales relation: { (item-name, color, size), (item-name, color), (item-name, size), (color, size), (item-name), (color), (size), ( ) } where ( ) denotes an empty group by list.For each grouping, the result contains the null value for attributes not present in the grouping.

اسلاید ۱۵: Extended Aggregation (Cont.)Relational representation of cross-tab that we saw earlier, but with null in place of all, can be computed byselect item-name, color, sum(number) from sales group by cube(item-name, color)The function grouping() can be applied on an attributeReturns 1 if the value is a null value representing all, and returns 0 in all other cases. select item-name, color, size, sum(number), grouping(item-name) as item-name-flag, grouping(color) as color-flag, grouping(size) as size-flag, from sales group by cube(item-name, color, size)Can use the function decode() in the select clause to replace such nulls by a value such as allE.g. replace item-name in first query by decode( grouping(item-name), 1, ‘all’, item-name)

اسلاید ۱۶: Extended Aggregation (Cont.)The rollup construct generates union on every prefix of specified list of attributes E.g. select item-name, color, size, sum(number) from sales group by rollup(item-name, color, size)Generates union of four groupings: { (item-name, color, size), (item-name, color), (item-name), ( ) }Rollup can be used to generate aggregates at multiple levels of a hierarchy.E.g., suppose table itemcategory(item-name, category) gives the category of each item. Then select category, item-name, sum(number) from sales, itemcategory where sales.item-name = itemcategory.item-name group by rollup(category, item-name)would give a hierarchical summary by item-name and by category.

اسلاید ۱۷: Extended Aggregation (Cont.)Multiple rollups and cubes can be used in a single group by clauseEach generates set of group by lists, cross product of sets gives overall set of group by listsE.g., select item-name, color, size, sum(number) from sales group by rollup(item-name), rollup(color, size) generates the groupings {item-name, ()} X {(color, size), (color), ()} = { (item-name, color, size), (item-name, color), (item-name), (color, size), (color), ( ) }

اسلاید ۱۸: RankingRanking is done in conjunction with an order by specification. Given a relation student-marks(student-id, marks) find the rank of each student.select student-id, rank( ) over (order by marks desc) as s-rank from student-marksAn extra order by clause is needed to get them in sorted orderselect student-id, rank ( ) over (order by marks desc) as s-rank from student-marks order by s-rankRanking may leave gaps: e.g. if 2 students have the same top mark, both have rank 1, and the next rank is 3dense_rank does not leave gaps, so next dense rank would be 2

اسلاید ۱۹: Ranking (Cont.)Ranking can be done within partition of the data.“Find the rank of students within each section.”select student-id, section, rank ( ) over (partition by section order by marks desc) as sec-rank from student-marks, student-section where student-marks.student-id = student-section.student-id order by section, sec-rankMultiple rank clauses can occur in a single select clauseRanking is done after applying group by clause/aggregation

اسلاید ۲۰: