It is both common and useful to collect large amounts of data over a long period of time for a wide variety of applications. For example, data tracking stock prices, ozone levels, room temperatures, server usage, etc. can be collected and analyzed for any number of reasons—both personal and professional. However, as the number of data points grows, the sheer volume of data can make viewing the data difficult and cumbersome. Typically, large time series data is presented in a single resolution, linear fashion. For example, the fluctuations in a stock price over time are often presented by plotting evenly dispersed time points against stock price in a X-Y graph.
If, as often happens, there are more data points than can reasonably be displayed in the allocated display space (e.g. a sheet of paper, a window on a computer screen), the user is typically provided with two options: first, the data is maintained at the same resolution and the display space is enlarged (e.g. by adding another sheet of paper or by increasing the size of the window and providing the user with scroll bars to move around the window); or second, the data is provided at reduced-resolution (e.g. 1 month's worth of data may show data points at 1-day intervals while a year's worth of data may show data points at 1-week intervals.)
However, forcing users to switch between different layouts at different resolutions is cumbersome and often makes it difficult to maintain data in context. Accordingly, it would be desirable to provide improved methods and display systems for viewing long time series data in a manageable and convenient way.
The present disclosure provides methods and systems for employing time relevance-based nonlinear distortion techniques to represent large time series data in a user-friendly manner. According to one aspect of the invention, the goal is to provide the user with an easy to understand representation of a large amount of data that allows the user to view all the data in a single display, but in different contexts according to the importance of the data.
For the purposes of the present disclosure, the term “display” includes any visual representation of the data, regardless of the format in which it is delivered or otherwise made viewable. Accordingly, a display may be provided on or included as part of a computer monitor, a handheld device (PDA, cell phone, etc.) screen, paper, pamphlet, projection, hologram, watermark, etc.
Referring first to
According to one embodiment, a multi-resolution index (MRi) may be created which assigns relative data weights and space weights to each of the data points in a given dataset. According to one embodiment, the relative data weight and space weights assigned are based on each data point's time-relevance. For example, a given multi-resolution display may include a Multi-Resolution index (MRi) that is defined using relative weights for data and display such as:
Accordingly, when the MRi is defined as above, the entire time series dataset is divided into three subsets, MR-0, MR-1 and MR-2. MR-0 contains the most recent data and the data is displayed at a resolution such that each unit of display space represents only 1 unit of data. MR-1 contains intermediate time data and is displayed at a resolution such that each unit of display space represents four units of data. Finally, MR-2 contains the oldest data and is displayed at a resolution such that sixteen units of data are represented in a single unit of display space.
Of course it should be appreciated that while this example utilizes time-relevance based divisions which, for ease of description, are described using terms such “Most recent”, “oldest,” etc., other embodiments may be employed using similar techniques that select other data bounds by which to assign position as well as relative data weights and space weights to the data.
At 14, the total display area is divided into substantially equally sized partitions in order to provide a distinct display area for each subset of data. Turning now to
Returning to
According to one embodiment, the datasets in each of the partitions are mutually exclusive. In other words, data that is presented in partition 25 is not represented in partitions 26 and 27, data that is presented in partition 26 is not represented in partitions 25 and 27, and data that is presented in partition 27 is not represented in partitions 25 and 26.
Returning to
It should be noted that the dimensionality (number of rows and columns) of each grid need not necessarily be dependant on the dimensionality of the grids in the same multi-resolution display. For example, as shown in
Accordingly, it will be appreciated that each partition may include a grid having any dimensions and that the number of grid cells and configuration thereof within a given partition may also be determined by the desired resolution and amount of data to be displayed within the partition. Accordingly, a given grid cell may have any desired shape or size.
The MR display described herein may be used to show static data, (i.e. data collected from time point A to time point B). Alternatively, the MR display may be an interactive display used to show dynamic data, (e.g. data collected in real time) by advancing the data through the display.
According to one embodiment where the display is an interactive display used to show dynamic data, the grid sizes are fixed and only a limited number of data points may be shown in the display at any time. For example, the display shown in
Alternatively, the grid sizes may be dynamic and any number of data values may be allowed to cumulate in the display. As data points are added, all of the data points in the data set would be redistributed in proportion to the data weights of the partitions, as indicated in the MRi.
As shown, the partitions may be laid out, for example from left to right, with the left-most partition containing the most important data, which in some embodiments may correlate to the most recent data, at the highest resolution and the right-most partition containing the least important data, which is some embodiment may correlate to the oldest data, at the lowest resolution. Accordingly, if the data points shown in
As stated above,
Thus, in a multi-resolution display using the exemplary MRi above, each partition would include 4× as much data as the its neighbor to the left. Accordingly, as the partitions are viewed from left to right, each partition includes increasingly more data, allowing the user to view the most current data at the highest resolution, while simultaneously viewing intermediate and old data, placed in context. Of course it will be appreciated that such an approach may be utilized for any n-fold MRi.
Returning to
The color may be derived from an appropriately chosen color map, which is a function that maps the values of a normalized data range to a color value, i.e.:
colormap::x→{r,g,b}, xε[0 . . . 1]
for the color represented in RGB color space.
As such, the cells in a matrix could range in color from red to yellow to green (and shades in between) where a red-colored cell indicates that the cell represents high or large data values, a yellow-colored cell indicates that the cell represents medium data values and a green-colored cell indicates that the cell represents low data values.
For example, if
Finally, at 22, the color coded multi-resolution display is output to the user. The MR display may be output using any suitable manner and on any suitable device including, but not limited to, on a computer monitor, handheld device such as a PDA or cell phone, printed on paper, etc.
Multiple MR displays may be displayed together in a single matrix to allow a user to compare time-series data collected from multiple sources. An example of this is shown in
According to another embodiment, instead of showing the data represented in each partition in a grid, the data may be presented using a calendar-bar-based technique. This technique uses the same steps shown in
An exemplary calendar-based matrix 30 is shown in
In
Of course it will be appreciated that while the display discussed in the present disclosure will likely include at least two time intervals, the display could include three, four, five, or as many more intervals as desired. Various factors may be used to select the desired number of intervals including, but not limited to, display screen size, relevance of the data, and the user-friendliness of the number of intervals selected.
As shown in
Moving one column to the right, each partition in column 34 represents a month's (in this case a month has been simplified to a 4 week period) worth of data for each computer and includes four units (e.g. vertical rectangles 44, 46, 48, and 50) which are then each subdivided into 7 cells (e.g. horizontal rectangles 52, 54, . . . 64 in rectangle 44). In this case, each unit in partition 34 represents a week's worth of data and each cell within the unit (e.g. rectangles 52-64) represents data from a day in the corresponding week.
Again moving one column to the right, each partition in column 36 represents a year's worth of data for each computer and includes 12 units (vertical rectangles 66, 68 . . . 88), which are then each subdivided into 4 cells (e.g. smaller rectangles 90, 92, 94, and 96 in unit 66). In this case, each unit represents a month's worth of data and each cell within the unit represents data from a week in the corresponding month.
It will be appreciated that any suitable layout may be selected to show the time-relevance based groupings in the partitions. For example, depending on the amount and type of data that will be displayed in a given partition, each unit may be displayed as a square, rectangle, portion of a pie chart, bar in a bar graph, etc. Moreover, this decision may be made on a partition by partition basis, as a partition that has 7 units may be more easily represented by 7 horizontal or vertical rectangles and a partition that has 24 units may be more easily represented by a 4×6 array of squares or rectangles. Accordingly, it will be understood that the particular layouts shown in the figures are intended only as examples of layouts that could be used and that no particular requirement or limitation is intended by the inclusion of these particular layouts and the exclusion of any other layout.
As with the displays shown in
For example, in
An additional or alternative embodiment is shown in
Accordingly, if desired, the amount of space a particular data unit takes up within its available space may be used to represent one aspect of the data in that grouping (e.g. cumulative data value, total number of shares traded in the depicted time period, highest temperature recorded during the time period) and color coding could be used to display another aspect (e.g. highest value, average data value, mean data value, average price per stock, average temperature per period, etc.)
It can also be seen by comparing rectangles 120 and 122, that the physical arrangement of the subdivisions within a rectangle need not be the same (i.e. all horizontal, all vertical, etc.). In this case, for example, the division of rectangle 122 into 7 horizontal segments would have made the individual segments too thin, so the segments were drawn vertically.
Moreover, while the embodiments in
While the invention has been described with reference to the exemplary embodiments thereof, those skilled in the art will be able to make various modifications to the described embodiments without departing form the true spirit and scope of the disclosure. Accordingly, the terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. For example, while the description may include absolute terms such as “oldest,” “most,” “smallest,” etc., it should be understood that such terms are used for descriptive purposes and should not be considered in a limiting sense.
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