It is common to analyze large data sets in the process of making business decisions. Such data sets may be thought of as comprising a dimensionally-modeled fact collection. For example, each “record” of the fact collection may represent attributes of an “item” or “entity” such as a particular user of online services, whereas the value at each field of the record represents a value of a particular characteristic of that entity (e.g., age of user, gender of user, number of online page views by that user, etc.). It is known to provide a visual representation of the dimensionally-modeled fact collections as an analysis tool for use in the process of making business decisions.
When interacting with and/or analyzing large data sets, each data set may have many record—millions or more. It can be difficult or impractical to consider all the records individually. Thus, for example, users may prefer to aggregate records together based on values of a particular one or more of the characteristics of the item represented by the record.
It is desirable to provide tools that facilitate the definition of such aggregation.
Records representing items in a dimensionally-modeled fact collection may be assigned to bins. A count-based portion of a user interface is operated to receive user indication of bin assignment specification of the records based on user-specified at least one count of records within the bins. Actual counts at which to assign the records to bins are determined by at least constraining records having a same data value at a specified particular dimension to be within the same bin, such that the determined actual counts of records at the particular dimension within each bin may be different from the user specified at least one count. A user-observable indication of the determined actual counts is provided.
The user interface may also include a value-based portion. The value-based portion of the user interface may be operated to receive user indication of bin assignment specification of records based on user-specified at least one value at the particular dimension. Determining actual counts includes reconciling the user indication of bin assignment specification in the count-based portion with the user indication of bin assignment specification in the value-based portion.
In accordance with an aspect of the invention, a user interface to a program executing on one or more computing devices is provided via which users may interact with a dimensionally-modeled fact collection representing a plurality of items and, more particularly, to process the dimensionally-modeled fact collection to specify a desired aggregation of the items. The thus-processed dimensionally-modeled fact collection may then be, for example, represented as a visual display or otherwise processed (e.g., by the at least one computing device). For example, the aggregation may be represented in tabular form or in a graphical form, such as in a histogram graph or box plot.
When aggregating the data into bins, a user may wish to cause divisions to be created that accurately reflect the distribution in the underlying data, so as not to obscure patterns in the original data set. Towards this end, knowing the count of records associated with a given frequency divisions assists in creating more useful bins.
For example, a user may specify at least one count of records within each bin, while the specified counts may be adjusted to determine actual counts by at least constraining records having a same data value at a specified particular dimension to be within the same bin. Thus, the determined actual counts of records at the particular dimension within each bin may be different from the user specified at least one count. The at least one count and the determined actual counts may be represented by a value that is an indication of a number of the records in a bin relative to the total number of records or may be represented by a value that is indication of an absolute number of the records in a bin. In accordance with an aspect, the display to a user dynamically updates an absolute count corresponding to a relative count, and vice versa.
Furthermore, a value specification portion may be provided for a user to specify data values in a particular dimension to specify bins such as, for example, specifying fixed “cut points” of the bins that are values of the records at a particular dimension specified to be dividing data values between bins or, as another example, specifying ranges of such data values for the bins. As a user adjusts a data value specification (e.g., cut point and/or range), one or more counts (e.g., absolute and/or relative) may be dynamically updated. Furthermore, as a user adjusts a count, one or more data value specifications may be dynamically updated. That is, the user may specify data values and/or counts, and changes to either data values or counts may automatically adjust the other, giving feedback as to the effect of change. Additionally, as above, depending on the distribution of data values, bins based on data values may not exactly match a specific count and, in these examples, a “true” count corresponding to the data value specification may also be indicated.
While a particular item of the dimensionally-modeled fact collection may represent many attributes of an item, for simplicity of illustration, we present an example focusing on one particular dimension representing an “age” attribute, to process a dimensionally-modeled fact collection to define a desired aggregation of the items. In the example, the dimensionally-modeled fact collection contains twenty one items that, in no particular order, have the following values at the age dimension:
10, 11, 10, 50, 30, 34, 24, 67, 45, 4, 32, 45, 78, 32, 12, 4, 3, 34, 65, 24, 54
After sorting the items by value of the age dimension, the twenty-one age dimension values are the following:
3, 4, 4, 10, 10, 11, 12, 24, 24, 30, 32, 32, 34, 34, 45, 45, 50, 54, 65, 67, 78.
As mentioned above, in the
Referring to the
Furthermore,
While
For simplicity of illustration, the
At 304, the items and desired nominal count indications are processed to determine corresponding actual count indications for binning. At 306, presentation is caused of the actual count indications for binning, as well as corresponding value indications of the bins. Referring to the
An example algorithm to determine corresponding actual count indications for binning is now described, based on either a count-based user selection or a value-based user selection:
1. Sort the list of values to be x(1), x(2), . . . , x(N), where N=#values in the data set and x(i)<x(i+1) for i=1, 2, . . . , N−1.
For count-based user selection:
2. user specifies desired # values (count) in bins to be a(1), a(2), . . . , a(M), where M=#bins;
3. actual count for each bin is determined to be b(1), b(2), . . . , b(M) where sum ((|a(j)−b(j)|) ^2) for j=1, 2, . . . , M is minimized and x(i)<x(i+1), where x(i) is in bin b(j) and x(i+1) is in bin b(j+1) for j=1, 2, . . . , M−1.
4. actual cut values (points) for each bin are determined to be {x(sum(b(0), b(1), b(2), . . . , b(k))), . . . , x(sum(b(0), b(1), b(2), . . . , b(k), b(k+1))−1)} where k=0, 1, 2, . . . M−1 and b(0)=0. The last value x(N) is placed in bin b(M).
For value-based user selection:
2. user specifies desired bin cut values to be c(0), c(1), c(2), . . . , c(M), where M=#bins and c(k)<c(k+1) for k=0, 1, . . . , M−1, and c(0)=x(1) and c(M)=x(N);
3. actual values in each bin k will be c(k)=<{x(i), x(i+1), . . . }<c(k+1) for k=0, 1, . . . , M−1. The last value x(N) is placed in bin M.
4. actual count for each bin k is determined to be count of x values in c(k)=<{x(i), x(i+1), . . . }<c(k+1) for k=1, . . . , M−1 and x(N) is in bin M.
We have thus described an apparatus/method such that, when aggregating items of a dimensionally-modeled fact collection into bins, user choices are guided/adjusted to more faithfully reflect the distribution in the underlying items, to minimize obscuring of patterns in the original data set. For example, adjusting the count of records associated with a desired nominal count of records for binning of the items may assist in determining binning that may increase the usefulness of the binning for analysis of the items. Furthermore, by allowing a user to also specify binning parameters based on values of the items in a particular dimension, and concomitantly providing indications of counts of records corresponding to the specified value-based binning parameters, user flexibility for binning specification may be enhanced.
Number | Name | Date | Kind |
---|---|---|---|
5308058 | Mandel et al. | May 1994 | A |
5328169 | Mandel | Jul 1994 | A |
5342034 | Mandel et al. | Aug 1994 | A |
5358238 | Mandel et al. | Oct 1994 | A |
5390910 | Mandel et al. | Feb 1995 | A |
5435544 | Mandel | Jul 1995 | A |
5547178 | Costello | Aug 1996 | A |
5550964 | Davoust | Aug 1996 | A |
6278989 | Chaudhuri et al. | Aug 2001 | B1 |
6351754 | Bridge et al. | Feb 2002 | B1 |
6438552 | Tate | Aug 2002 | B1 |
6499032 | Tikkanen et al. | Dec 2002 | B1 |
6505206 | Tikkanen et al. | Jan 2003 | B1 |
6549910 | Tate | Apr 2003 | B1 |
6865567 | Oommen et al. | Mar 2005 | B1 |
6907422 | Predovic | Jun 2005 | B1 |
7197513 | Tessman et al. | Mar 2007 | B2 |
7209924 | Bernhardt et al. | Apr 2007 | B2 |
7246014 | Forth et al. | Jul 2007 | B2 |
7342929 | Bremler-Barr et al. | Mar 2008 | B2 |
7562058 | Pinto et al. | Jul 2009 | B2 |
20020077997 | Colby et al. | Jun 2002 | A1 |
20030076848 | Bremler-Barr et al. | Apr 2003 | A1 |
20040002980 | Bernhardt et al. | Jan 2004 | A1 |
20050068320 | Jaeger | Mar 2005 | A1 |
20050240456 | Ward et al. | Oct 2005 | A1 |
20060028470 | Bennett et al. | Feb 2006 | A1 |
20060036639 | Bauerle et al. | Feb 2006 | A1 |
20060085561 | Manasse et al. | Apr 2006 | A1 |
20070244849 | Predovic | Oct 2007 | A1 |
20080016041 | Frost et al. | Jan 2008 | A1 |
20080104101 | Kirshenbaum et al. | May 2008 | A1 |
Number | Date | Country | |
---|---|---|---|
20080294595 A1 | Nov 2008 | US |