Dimensional model organize life folderarchy system-neo-tech Archives - Page 3 of 4 - Cosmic Elevator

The other day I went to the local thrift store down the street. I don't know what it is about Seattle, but the thrift stores are always jumpin', and not just with the down and out who need a new pair of shoes but with people who could totally pay for stuff at the mall. I needed an egg cup, a DVD player, and a sweater, so I figured these might be the kinds of items you would find at a thrift shop. I found an egg cup and a nice sweater but there were no DVD players in sight. Lots of VCRs, though.

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In-fact we found that organising the contracts Dimensional model organize life folderarchy system with security policies applied dynamically to each agreement was the best approach for those rare instances where the user would browse to find an agreement. The browsing structure of the repository provides context. Dimensions have several intrinsic characteristics that are important for data analysis: Referential integrity. You would define attributes for the descriptive labels. Document organization provides context organie the information. Sometimes all Dimensional model organize life folderarchy system documents may be of the same foldwrarchy or owned by the same group, but not necessarily. Relations are identical to variables except in their data type. Like an open organization with boundary spanning sub-systems, learning organizations emulate the best practices from other comparable organizations, understanding how market and other economic factors have been successfully incorporated by them and what new practices they have adopted to satisfy the needs of die customers foldearchy suppliers. This is a really useful blog posting. This is usually found when an Nude scenes from american gangster system has been implemented for a particular point solution and the documents are organized as needed moeel the business solution.

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  • Tree structures let us take a large number of things and organise them in a way that makes sense.
  • This chapter describes the multidimensional data model and how it is implemented in relational tables and standard form analytic workspaces.
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  • While working with a couple of clients recently the question of how best to organize documents in their ECM systems came up.

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For example, Q and Q are the children of , thus is the parent of Q and Q In an analytic workspace, facts are stored in variables, typically with a numeric data type. Managers and team leaders have to develop leadership skills, and coaching skills to exploit the learning opportunities. Then dimensional tables are generated that are in the "everything in one row" format that is so easy to use in excel, either through an auto-filter, or with pivot tables. Browsing is better when looking for documents related to one another, such as all the documents related to an insurance claim. Time attributes can provide information about the Time dimension that may be useful in some types of analysis, such as identifying the last day or the number of days in each time period.

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For every object you store one row recording the parent of the object. This means that every relationship in the tree is stored only once. The downside of this structure is that it requires looking at multiple rows to summarise data. And its just not easy to read. If a hierarchy has 10 levels, we have to look at ten rows for every row that we want to summarise to the top level.

Not so good. This way, we have everything right there and it makes it easy to summarise. To find the totals for a country just add up every row with a given value in the country field. Then dimensional tables are generated that are in the "everything in one row" format that is so easy to use in excel, either through an auto-filter, or with pivot tables. It's been over 15 several hours since EA announced the newest Road Rash was birth. Hopefully they're working on it as we speak.

In the logical multidimensional model, a cube represents all measures with the same shape, that is, the exact same dimensions. In a cube shape, each edge represents a dimension. The dimension members are aligned on the edges and divide the cube shape into cells in which data values are stored. In an analytic workspace, the cube shape also represents the physical storage of multidimensional measures, in contrast with two-dimensional relational tables.

This is an important part of multidimensional data storage, calculation, and display, because different analysts need to view the data in different ways. For example, if you are the Sales Manager for the Pacific Rim, then you need to look at the data differently from a product manager or a financial analyst. Assume that a company collects data on sales.

The company maintains records that quantify how many of each product was sold in a particular sales region during a specific time period. You can visualize the sales measure as the cube shown in Figure Figure compares the sales of various products in different cities for January shown and February not shown.

This view of the data might be used to identify products that are performing poorly in certain markets. Figure shows sales of various products during a four-month period in Rome shown and Tokyo not shown. This view of the data is the basis for trend analysis.

A cube shape is three dimensional. Additional dimensions are pictured with additional cube shapes. Figure shows how dimension, variable, formula, and relation objects in a standard form analytic workspace are used to implement the multidimensional model.

Measures with identical dimensions compose a logical cube. All dimensions have attributes, and all hierarchical dimensions have level relations and self-relations; for clarity, these objects are shown only once in the diagram. Variables and formulas can have any number of dimensions; three are shown here. A dimension in an analytic workspace is a highly optimized, one-dimensional index of values that serves as a key table. Variables, relations, formulas which are stored equations are among the objects that can have dimensions.

Referential integrity. Each dimension member is unique and cannot be NA that is, null. If a measure has three dimensions, then each data value of that measure must be qualified by a member of each dimension.

Likewise, each combination of dimension members has a value, even if it is NA. Dimensions are maintained as separate containers and are shared by measures. Measures with the same dimensionality can be manipulated together easily.

Preserved order of members. Each dimension has a default status , which is a list of all of its members in the order they are stored. The default status list is always the same unless it is purposefully altered by adding, deleting, or moving members.

Within a session, a user can change the selection and order of the status list; this is called the current status list. The current status list remains the same until the user purposefully alters it by adding, removing, or changing the order of its members.

Because the order of dimension members is consistent and known, the selection of members can be relative. For example, this function call compares the sales values of all currently selected time periods in the current status list against sales from the prior period. Highly denormalized. A dimension typically contains members at all levels of all hierarchies.

This type of dimension is sometimes called an embedded total dimension. In addition to simple dimensions, there are several special types of dimensions used in a standard form analytic workspace, such as composites and concat dimensions. These dimensions are discussed later in this guide. In an analytic workspace, data dimensions are structured hierarchically so that data at different levels can be manipulated for aggregation, allocation, and navigation.

However, all dimension members at all levels for all hierarchies are stored in a single data dimension container. For example, months, quarters, and years are all stored in a single dimension for Time. The hierarchical relationships among dimension members are defined by a parent relation, described in "Analytic Workspace Relations". Not all data is hierarchical in nature, however, and you can create data dimensions that do not have levels. A line item dimension is one such dimension, and the relationships among its members require a model rather than a multilevel hierarchy.

The extensive data modeling subsystem available in analytic workspaces enables you to create both simple and complex models, which can be solved alone or in conjunction with aggregation methods. As a one-dimensional index, a dimension container has many uses in an analytic workspace in addition to dimensioning measures.

A standard form analytic workspace uses dimensions to store various types of metadata, such as lists of hierarchies, levels, and the dimensions composing a logical cube. A variable is a data value table, that is, an array with a particular data type and indexed by a specific list of dimensions. The dimensions themselves are not stored with the variable. Each combination of dimension members defines a data cell, regardless of whether a value exists for that cell or not.

Thus, the absence of data can be purposefully included or excluded from the analysis. For example, if a particular product was not available before a certain date, then the analysis may exclude null values called NA s in the prior periods.

However, if the product was available but did not sell in some markets, then the analysis may include the NA s. No special physical relationship exists among variables that share the same dimensions.

However, a logical relationship exists because, even though they store different data that may be a different data type, they are identical containers. Variables that have identical dimensions compose a logical cube. If you change a dimension, such as adding new time periods to the Time dimension, then all variables dimensioned by Time are automatically changed to include these new time periods, even if the other variables have no data for them.

Variables that share dimensions and thus are contained by the same logical cube can also be manipulated together in a variety of ways, such as aggregation, allocation, modeling, and numeric calculations. This type of calculation is easy and fast in an analytic workspace, while the equivalent single-row calculation in a relational schema can be quite difficult.

In an analytic workspace, facts are stored in variables, typically with a numeric data type. Each type of data is stored in its own variable, so that while sales data and expenses data might have the same dimensions and the same data type, they are stored in two distinct variables. The containers are identical, but the contents are unique. An analytic workspace provides a valuable alternative to materialized views for creating, storing, and maintaining summary data.

A very sophisticated aggregation system supports modeling in addition to an extensive number of aggregation methods. Pre-aggregated data is stored in a compact format in the same container as the base-level data, and the performance impact of aggregating data on the fly is negligible when the aggregation rules have been defined according to known good methods. If aggregate data needed for the result set is stored in the variable, then it is simply retrieved. If the aggregate data does not exist, then it is calculated on the fly.

Like measures, attributes are stored in variables. However, there are significant differences between attributes and measures. While attributes are often multidimensional, only one dimension is a data dimension.

A hierarchy dimension, which lists the data dimension hierarchies, and a language dimension, which provides support for multiple languages, are typical of the other dimensions.

Attributes provide supplementary information about each dimension member, regardless of its level in a dimension hierarchy. For example, a Time dimension might have three attribute variables, one for descriptive names, another for the period end dates, and a third for period time spans. All of the other days, months, quarters, and years in the Time dimension have similar information stored in these three attribute variables. A formula is a stored equation.

In this way, a formula in an analytic workspace is like a relational view. In a standard form analytic workspace, one of the uses of a formula object is to provide the interface between aggregation rules and a variable that holds the data.

The name of a measure is always the name of a formula, not the underlying variable. While the variable only contains stored data the base-level data and precalculated aggregates , the formula returns a fully solved measure containing data that is both stored and calculated on the fly.

This method enables all queries against a particular measure to be written against the same column of the same relational view for the analytic workspace, regardless of whether the data is calculated or simply retrieved. That column presents data acquired from the formula. Relations are identical to variables except in their data type.

The dimension members provide a finite list of acceptable values for a relation. A relation container is like a foreign key column, except that it can be multidimensional.

In a standard form analytic workspace, two relations support the hierarchical content of the data dimensions: a parent relation and a level relation.

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The other day I went to the local thrift store down the street. I don't know what it is about Seattle, but the thrift stores are always jumpin', and not just with the down and out who need a new pair of shoes but with people who could totally pay for stuff at the mall. I needed an egg cup, a DVD player, and a sweater, so I figured these might be the kinds of items you would find at a thrift shop.

I found an egg cup and a nice sweater but there were no DVD players in sight. Lots of VCRs, though. In the electronics section, I saw a flash of yellow.

Since I received the iPod as a prize at work back in , I had no idea how much they really cost. It's a bit bulkier but I still have a lot of tapes since I have a tape player in my car and I grew up in the late 80s- early 90s when the tape was the format.

In the meantime, I could also take it running and to the gym! Coming soon to this space: Thoughts on the lost art of the mixtape Very interesting site!