WO2001093123A2

WO2001093123A2 - Generating image data

Info

Publication number: WO2001093123A2
Application number: PCT/GB2001/001853
Authority: WO
Inventors: Jonathan Bruce Mullins; Clement Hadrian Chambers
Original assignee: Advfn.Com Plc
Priority date: 2000-05-26
Filing date: 2001-04-27
Publication date: 2001-12-06
Also published as: GB0012909D0; AU5051901A

Description

Generating Image Data

Field of the Invention

The present invention relates to generating image data from data items wherein said data items have a plurality of numeric fields. In particular the invention relates to generating image data by processing numerical data having a plurality of dimensions.

Introduction to the Invention An abundance of numerical data is available for analysis using a wide variety of mathematical methods. The abundance of data has arisen from the implementation of systems capable of digitally sampling real world events, and of storing an unlimited amount of this data. Many systems yield useful information as a result of the mathematical analysis that may be performed upon data of this kind. For example, several types of statistical analysis may be performed on numerical data created when measuring performance of critical machinery such as jet engines, new materials for constructing buildings, or the modelling of complex systems such as telecommunications networks. Historically, computers have been successfully used in numerous scientific and mathematical applications. However, certain natural systems have been found to be largely resistant to numerical analysis. The study of weather systems and financial markets has led to the development of chaos theory and the study of the behaviour of unpredictable systems. A related field is the study of fractals, which aims to predict the behaviour of chaotic systems using mathematical tools outside those of pure statistics. Although progress in chaos theory and fractals has improved the predictability of complex systems behaviour, these new mathematical tools have also revealed fundamental limitations in the degree to which the behaviour of such systems can be predicted.

Although complex systems analysis has benefited immensely from the introduction of computers, a significant human input is still found to be necessary. In financial markets, an experience of stock market fluctuations over many years enables an individual trader to make quick intuitive decisions that are considerably better judged than any formal analysis could provide. The ability to perform in this way requires considerable awareness of many aspects of financial markets, as well as experience and familiarity with mathematical procedures, in order to anticipate trends.

An example of this kind of trading is arbitrage. In arbitrage, a trader may buy a first currency, for example dollars, convert the dollars to a second currency, for example yen, and then convert the yen back into the original currency, such as pounds sterling. Depending upon market conditions, it is possible to finish with more pounds sterling than the trader originally started with. A trader experienced in arbitrage can spot combinations of trends, behaviours and conditions, and quickly make a sequence of trades that results in profit. This type of action requires an acute awareness of market conditions as they change in real time. While computers are not capable of generally predicting the behaviour of complex systems, in financial trading they have a central role to play in the presentation of information. With a large high definition monitor and a fast network connection, todays analysts and traders have instant access to whatever financial information they require. Due to the diversity of data, information of this type is usually supplied in the form of numbers. A particular sequence of numbers, for example share prices in a particular company or stock exchange index, can be converted into a graph using readily available spreadsheet graphing facilities. Complex numerical analysis tools may be applied to such data, including the fractal analysis methods developed by Benoit Mandelbrot and others. The results of analysis can also be graphed without difficulty.

These methods are widely applied in stock markets. However, due to the complexity of data that is available, there is no known way of providing a unified view of diverse data, beyond that of the simple graph. This is a problem inherent in the data itself, resulting from its origination in a complex dynamic system.

Summary of the Invention

According to an aspect of the present invention, there is provided a method of generating image data from a plurality of data items, in a data processing system including memory means for storing instructions and said data items, and processing means for generating said image data; wherein said instructions define operations to be performed by said processing means to generate said image data from said data items; said data items have at least one numerical field representing financial data and said data items are modelled as objects in a three dimensional world view; and said operations include an initial step of defining positions of said objects in said three dimensional world view; with subsequent steps applied to each object of: defining a movement in response to an accelerating force identified with reference to said financial data and at least one other object; drawing the object in its new position; and a final step, for all objects, of repeating said defining and drawing steps.

Brief Description of the Drawings

Figure 1 shows the Internet, including a data service provider supplying data items to users at several connected computer terminals; Figure 2 summarise the present invention, in which image data is generated from data received from the data service provider shown in Figure

1;

Figure 3 details a terminal of the type shown in Figure 1, including a computer and a monitor;

Figure 4 details hardware components of the computer shown in Figure 3, including a main memory;

Figure 5 summarises steps performed at the computer terminal shown in Figure 2, including a step of generating image data; Figure 6 details contents of the main memory shown in Figure 4 during the step of generating image data shown in Figure 5;

Figure 7 concurrent steps performed during the step of generating image data shown in Figure 5, including a step of generating a view;

Figure 8 summarises the views that may be generated during the step of generating a view shown in Figure 7, including a grid view, a planets view, a nails view, a particles view, an a top list view;

Figure 9 details data of the type received from the data service provider shown in Figure 1;

Figure 10 details a data item resulting from combining data of the type shown in Figure 9;

Figure 11 details a first aspect of the grid view indicated in Figure 8, including a first numeric field and a second numeric field;

Figure 12 details a second aspect of the grid view shown in Figure 8, including weights for additional numeric fields; Figure 13 details the planets view indicated in Figure 8;

Figure 14 details the nails view indicated in Figure 8;

Figure 15 details the particles view indicated in Figure 8, at an initial condition; Figure 16 details the particles view indicated in Figure 8, at a final condition;

Figure 17 details the top list view indicated in Figure 8; Figure 18 summarises steps required to generate the planets view shown in Figure 13, including steps of drawing planets and drawing nails;

Figure 19 details the step of drawing planets shown in Figure 18; and Figure 20 details the step of drawing nails shown in Figure 18.

Detailed Description of The Preferred Embodiment The invention will now be described by way of example only with reference to the accompanying drawings.

Figure 1

An environment for distributing data is shown in Figure 1. A data service provider 101 supplies data in response to requests for data received from users on the Internet 102. Users are connected to the internet via Internet service providers (ISP) 111 to 116. Additionally, certain users may connect to an Internet service provider 114, 115 via an intranet 116 or 117, that is provided by a company or organisation for internal data communications. Users have computer terminals 121 to 135 that receive data from the data service provider 101.

Data from the data service provider 101 is supplied to computer terminals where the data is stored temporarily in the form of data items, each of which has a plurality of numerical data fields. In the present embodiment, each data item represents financial information about a company or other financial entity.

Data items comprise a plurality of numerical data fields that define various characteristics of a company's financial condition. These numerical data fields are collectively known as a company's fundamental data, or fundamentals. The fundamentals used by financial analysts or stock market traders in order to make informed decisions about market purchases and sales. The data service provider 101 supplies data in a data file standard known as comma separated values (CSV). Alternatively, proprietary formats may be used with appropriate conversion. A CSV file, once downloaded from the internet 102, can be imported into a spreadsheet application operating on any of the computer terminals 121 to 135. Thereafter, a user may configure the spreadsheet to perform known numerical analysis methods, and use built-in data visualisation functions to display graphs. However, the complexity of company fundamentals, comprising in the region of fifty or more parameters for each individual company, ensures that presenting the information is not, in itself, sufficient to enable a user to gain insight into hidden aspects of market behaviour.

Professional traders usually rely on an intimate knowledge of market behaviour gained over many years of experience in order to distinguish subtle trends and characteristics that may be of importance.

Figure 2

The invention is summarised in Figure 2. Incoming data 201 is received from the data service provider 101. The received data includes information about many hundreds or thousands of companies. The user identifies a subset of these, and this subset is considered as a set of data items 202. Each data item therefore corresponds to a company or other financial entity. Each data item has a large number of fundamentals that describe its trading and other economic characteristics. Of the many fundamentals that are available, the user selects three that are of interest. These are considered as the first numerical field 211 , second numerical field 212 and third numerical field 213. These numerical fields are then used to generate image data from each data item.

Data items are represented by particles 231 to 234 in a three- dimensional world view 235. The three dimensional world contains red, green and blue attractors. The first numerical field of each data item is multiplied by a user-defined scaling factor S1 to define an accelerating force of a particle towards the red attractor. The second numerical field of each data item is multiplied by a user-defined scaling factor S2 to define an accelerating force of a particle towards the blue attractor. The third numerical field of each data item is multiplied by a user-defined scaling factor S3 to define an accelerating force of a particle towards the green attractor. Accelerating forces may be considered as gravitational forces. These are combined to define a vector for each data item, and the positions of respective particles representing data items are updated accordingly.

The three dimensional view 235 is projected into a two dimensional view window in the user's monitor, and updated regularly in order to model the trajectories of data items.

Scaling values S1 , S2 and S3 may be adjusted to suit the optimum behaviour of the model. This is an interactive process, requiring a minimum of effort by the user. Switching between various combinations of settings of company selections, and scaling values, can be performed extremely quickly by loading and saving these settings in configuration files. The particles are drawn as small three dimensional spheres. Their surfaces are preferably colour coded in response to a financial characteristic, such as the market sector the data item belongs to. The rate at which time passes in the gravitational model may be adjusted by the user. Figure 3

A computer terminal of the type shown at 121, operated by the user while receiving data from the data service provider 101, is illustrated in Figure 3. A computer 301 contains processing and other circuitry required for connection to the Internet. The computer 301 receives commands from the user in the form of keystrokes made on a keyboard 302, and in the form of movements and button presses made on a computer mouse 303. Various forms of processing are performed by the computer 301 in order to interpret these commands, and this interpretation is dependent upon the context or state of the computer at the time the command is given. In this way, the input devices 302 and 303 provide a general means of user command input, that may be applied also to an embodiment of the present invention. In response to user commands, and the internal state of the computer 301 , image data is converted into signals supplied to a monitor 304, resulting in a display of images responsive to user operations and computer states.

Figure 4

The computer 301 shown in Figure 3 is detailed in Figure 4. A central processing unit (CPU) 401 performs processing in response to instructions stored in a main memory 402. The main memory comprises sixty-four megabytes of dynamic RAM. The central processing unit includes primary data and program cache circuitry to minimise the transfer time of repeatedly accessed data and instructions. A suitable processor is a Pentium III available from Intel Corporation. Secondary cache circuitry, also provided in the CPU 401 , operates at a lower speed, but at a larger memory capacity, thereby minimising transfer of data over a bus 403 electrically shared by several other components of the computer. A hard disk drive 403 provides non-volatile storage for CPU instructions and data, and further extends the effective capacity of main memory through the use of virtual addressing techniques. A graphics card 404 receives instructions for the rendering of image data generated by the CPU 401. In the graphics card 404, digital to analogue converters for each of red, green and blue colour components generate electrical signals that are supplied to the monitor 304 via an electrical connection 405. The graphics card 404 generates frames of image data at a resolution of 1024 by 768 pixels, where each pixel has a data depth of eight bits for each of the red, green and blue colour components. In the preferred embodiment, the graphics card 404 includes a processor configured to render image data supplied to the graphics card in the form of OPENGL graphics commands. However, in alternative embodiments it is possible for the CPU 401 to perform the processing required in order to implement OPENGL functionality, but at a slower speed.

OPENGL commands facilitate definition of shapes, objects and lighting in a virtual 3D space, that may be viewed in a conventional two dimensional window displayed on the monitor 304. A three dimensional window responsive to OPENGL commands is considered as having an OPENGL context, that in combination with other types of windows and contexts, facilitates user interaction with the virtual 3D world it contains.

Instructions for operations performed by the CPU may be installed from a CDROM drive 406. Instructions are usually stored in compressed form on a CDROM, and installed by a smaller number of installation instructions controlling the CPU 401 , so that new instructions are stored onto the hard disk drive 403. Thereafter, a user may initiate the new instructions by an appropriate context-dependent command on the keyboard 302 or mouse 303. CDROMs may also be used as a source of data, for example historical data relating to company performance. This historical data may extend to several hundred megabytes in size, if several years and several stock markets are represented. An alternative source of new instructions and or data is a local area network, such as a corporate intranet 116. Connectivity to an intranet is made via a network card 407, and an Ethernet connection 408.

Input output circuitry (I/O) 409 facilitates communication between the keyboard 302, mouse 303 and the CPU 401. A modem 410 facilitates connectivity to ISDN or a telephone line. Internet access may be achieved either via the modem 410 or the network card 407.

Figure 5

Operations performed by the user of the computer terminal 121 are summarised in Figure 5. At step 501 the computer is switched on and the processing system, comprising the CPU 401 and the main memory 402, are initialised. At step 592, if necessary, instructions for generating image data are loaded from a local area network or CDROM 503. At step 504 the user initiates instructions for generating image data. Thereafter, image data is generated in response to contexts and states generated by user commands and incoming data 505 received over the Internet 102, from the data service provider 101.

The service provider 101 supplies data to the user terminal 121 at a rate dependent upon a previously agreed contractual arrangement. Depending on the importance of the data, updates may be received once a day, or virtually continuously, with updates being received every minute or so. Having completed generating image data, several context settings, defined by the user, are saved at step 506. These may then be recalled during a later session. At step 507 the processing system is shut down, requiring a synchronisation of main memory 402 and the hard disk drive 403, in order to ensure that all permanent data have been stored in nonvolatile media.

Figure 6

Contents of the main memory 402 while generating image data 504 are detailed in Figure 6. A Windows NT (TM) operating system 601 provides common instructions, such as those required for accessing the hard disk drive 403, graphics card 404 and all components of the computer. The operating system 601 includes instructions for facilitating OPENGL rendering commands in co-operation with processes performed by the graphics card 404. Instructions for generating image data 602 facilitate reception of data from the data service provider 101, interaction with user commands supplied from the keyboard 302 and mouse 303, and generation of graphical contexts within which windows are generated for display on the monitor 304. These contexts include an OPENGL window, within which a projection of three-dimensional world space is viewed and adjusted. Other applications 603 include instructions for file management and data backup. Instructions 601, 602 and 603 are permanently stored on the hard disk 403, and are loaded on demand into the main memory 402. The operating system 601 is loaded automatically when the computer is switched on. Thereafter, sequences of instructions are selectively identified by the operating system for execution in short bursts of time, or time slices. This provides the illusion to the user, that the CPU 401 is executing multiple tasks concurrently. Furthermore, multi-tasking enables the computer to receive data updates 505 over the internet while at the same time generate image data 504 on the monitor 304 without the user being aware of any interruption.

During execution of instructions 601 to 603, portions of memory 402 are allocated and de-allocated as a result of changing instruction requirements. A dynamic workspace 604 is provided by the operating system 601 , in the form of a stack, to facilitate the allocation of memory in response to requests from applications 602, 603 as their instructions are executed. Other regions of memory are also allocated for specific requirements of the instructions for generating image data 602. An incoming data buffer 605 temporarily stores incoming data from the data service provider 101, received over the Internet. Data items 606 include data from the buffer 605 that have been formatted in such a way as to be suitable for generating image data. User preferences 607 include settings relating to generating image data, including the way in which data items 606 are generated from incoming buffered data 605.

Figure 7

The step of generating image data 504 shown in Figure 5 is detailed in Figure 7. At step 701 the user selects a view. At step 702 the appropriate view is generated, resulting in the generation of a three-dimensional view in a window on the monitor 304. Incoming data is received at step 703, and a database of updated stocks and shares of companies and other financial entities is stored in the incoming data buffer 605 in main memory. The user defines a data requirement at step 704, resulting in data items selected from the database 605 being stored as data items 606 in main memory 402. Selected data items usually include a subset of a much larger number of companies and financial entities whose fundamentals are stored in the buffer 605. The selected data items 606 are supplied as additional input data to the step of generating a view 702. Steps 701 to 704 each represent processing performed in a multitasking processing environment, as facilitated by the operating system instructions 601. These steps are not necessarily performed in any particular order, and at any time, the precise functionality implemented by any of these steps is defined by the data available to it. As an additional parallel process, step 705 saves user preferences to hard disk. User preferences include configuration data, that includes, for example, an indication as to which companies are to be selected as data items 606 when generating a view. Other data relating to specific views is also stored in association with a particular configuration, as will be detailed subsequently.

Figure 8

The step of generating a view 702 shown in Figure 7 is detailed in Figure 8. At step 801 a selection is made on the basis of the view identified by the user at step 701. Thereafter the appropriate view is selected from one of the following: grid view 802, planets view 803, nails view 804, particles view 805, top list view 806 and other view 807.

Figure 9

An example of the data received from the data service provider 101 is shown in Figure 9. Figure 9 details contents of a file that has been supplied in the comma separated value (CSV) format. A first column 901 defines the full names of companies, or other financial entities, such as trust funds and so on. A second column 902 specifies a unique symbol for the company or financial entity, comprising a few alphabetic characters. A third column 903 specifies the share price for the company. Share prices may fluctuate rapidly, and so it is common to receive a CSV file in which the only company fundamental defined is the share price. Other CSV files may include other company fundamentals, updated less frequently. Several data service providers supply data of this kind, varying in the cost and frequency of updates. A suitable source of such data is quote.com, and information relating to CSV formats and other implementation details is available at http://www.quote.com/quotecom/qcharts/qfeed.asp?=qfeed

Figure 10

A data item comprises many company fundamentals, assembled from information supplied, perhaps with frequent updates from several CSV files. A resulting data item is shown in Figure 10. A first column 1001 shows the name of the fundamental, and a second column 1002 shows the value of the fundamental. Typically, many thousands of companies will be represented in this way, not all of which will be of primary interest to the user. Also, only a few of the fundamentals will be of interest. Thus, the totality of company data is largely redundant, and decisions are most easily made by monitoring key companies or entities, and within these, only certain fundamentals. The data items 606 represent user-selected companies or financial entities, from which image data is to be generated. The fundamentals used to generate image data may be defined separately for each of the possible views 801 to 806.

Figure 11

The grid view 801 indicated at 801 in Figure 8 has an appearance, when displayed on the monitor 304, as shown in Figure 11. Five separate windows are displayed, including the view window 1101. A title bar 1102 includes drop-down menus for controlling various aspects of the windows that are being displayed. A file menu 1103 enables loading and saving of user preferences, stored as configurations. A configuration stores configurations for several windows, and for multiple views. An edit menu 1104 enables the user to modify the selection of data items currently used to generate display data. A views menu 1105 enables the user to switch between the available 3D views 801 to 806 for the selected data items. A window menu 1106 enables the user to edit items or characteristics in any of the windows that are currently active. A help menu 1107 provides context-sensitive help to enable the user to navigate the several windows and menus that are provided. An edit menu 1108 enables the user to set up equations for generating parameters in addition to those provided by the company fundamentals. Any of the fundamentals may be supplied as a variable to an equation defined by the user, the result of which may then be applied in the same way as a company variable when generating display data. A feed menu 1109 enables the user to configure aspects of the connection with the data service provider 101. For example, the user may wish to modify download settings so that company data is updated more frequently.

A data items window 1111 displays a list of currently selected data items that are being used to generate display data. Usually, more companies will be selected than can fit into the display, and the user may scroll through the list using a scroll bar or other scrolling means provided by the operating system 601. A show data window 1112 provides a short list of companies for which fundamentals are to be displayed. A single company selected in this window, for example MSFT, will be highlighted in some way in the view window 1101 , and its fundamentals will be displayed in a fundamentals window 1113.

A grid properties window 1114 enables the user to define which fundamentals are to be used for each dimension of the grid of data items that is displayed in the view window. In the present example, fourteen companies have been selected as data items from which display data is to be generated. These are arranged as elements within a four-by-four array. Each of the two dimensions of this array is associated with a particular fundamental, or user-defined value. The X dimension has its fundamental selected at 1121 , and the Z dimension has its fundamental selected at 1122. Examples shown are Market Cap and Dividend for X and Z dimensions respectively. However, any fundamental or user defined value may be used. The array is sorted so that companies have Market Cap values that increase in the X dimension, and Dividend values increase in the Z dimension. A third dimension, Y, is also defined, for which a different properties window is used.

Figure 12 A properties window for defining the Y dimension of the grid view

1101 shown in Figure 11 is shown in Figure 12. A weights properties window 1201 defines weighted fundamentals contributing to the Y dimension for each of the companies from which image data is to be generated. Three value fields 1211, 1212 and 1213 are provided in which the user defines three characteristics applied to a company fundamental.

These fields define the name of the fundamental 1221, whether or not to invert the fundamental 1222, and the level of weighting 1223 for the fundamental. In the simplest situation, only a single fundamental is used, in which case the weights in fields 1212 and 1213 are set to zero. The fundamental in field 1211 is multiplied by its weight 1223, and this value defines the extent of a company's height in the 3D bar graph shown in the view 1101. Inverting the fundamental, by ticking the box at 1222, has the effect of using the reciprocal of the fundamental, with the result multiplied by its weight 1223. A normalise values option 1224 causes the heights in the Y dimension, of each company on the grid, to be normalised in response to the height of the highest company, which is set to a value of one. When multiple fundamentals are configured to contribute to the Y dimension of a company in the grid view 1101 , this enables the user to set up sophisticated interrelations between fundamentals, that may be quickly and easily adjusted for maximum effectiveness when visualising company performance. Reconfiguration of the weights properties window 1201 and the grid properties window 1114 shown in Figure 11, enables the user to easily adapt and try out various methods for generating image data from company fundamentals.

In the grid view 1101 , or any of the 3D views, the user can modify the viewpoint in 3D space, so as to obtain a different perspective on the data items that have been rendered. For example, the viewpoint my be rotated left or right, up or down, with the grid of companies remaining at the centre. This may be achieved by moving the mouse cursor over the view window 1101, and then dragging the mouse 303 with its left button held down, left or right, up or down, with respect to the screen. The user can zoom in or out of the view window by dragging the mouse, but this time with the control key held down on the keyboard 302 as well as the mouse button. In this way the user may quickly and easily view the data that is being displayed from a number of different 3D perspectives, without the complexity of having to navigate the 3D environment with a special 3D input device such as a 3D mouse or a dataglove. Commands for modifying the viewpoint of a view in 3D space are provided by OPENGL 3D graphics instructions. The user's viewpoint is specified as a single set of x, y, z coordinates and a 2D projection of the 3D objects viewed from that point in world space is rendered.

In any of the views, the user may identify a particular company in the show data window 1112, and this company will be highlighted in the 3D view by changing its colour to a contrasting colour.

Figure 13

The planets view 802 indicated in Figure 8, is shown in Figure 13. The planets view window 1301 has a circular grid 1302. Each company or data item is represented as a planet orbiting the centre 1304 of the grid 1302. The centre is marked by a small yellow sphere 1304. The radius, or size, of a planet 1303 is controlled by a first company fundamental selected in the planet properties window 1305 in a first data entry field 1306. The distance of a planet 1303 from a first ring 1315 from the centre 1304 of the grid 1302 is determined by a second company fundamental selected in a second data entry field 1307. A reference point 1308 on the outer circumference of the grid 1302 provides a reference for a zero degree angular displacement. The reference point is also marked by a small yellow sphere. The angular displacement of a planet 1303 from the reference point 1308, around the grid, is controlled by a third company fundamental selected in a third data entry field 1309.

Each data entry field 1306, 1307 and 1309 includes a region 1310 for selecting the company fundamental and a region 1311 for a scaling factor that may be adjusted in accordance with the requirements of the company data from which image data is to be generated. The circular grid, the planets and the reference point rotate smoothly about the centre 1304 at a fixed rate, typically once every two seconds. The rate of rotation is set by a rotation parameter at 1312. Rotation enables the user to see the relationships and characteristics of data items from a continuously changing perspective. Because the orbit paradigm is used, the continuous rotation of perspective does not have the disconcerting effect it would have if applied to data items on non-circular grid. The vertical perspective position may be adjusted by dragging with the mouse in a vertical direction in the view window 1301.

Figure 14

The nails view 803 indicated in Figure 8 is detailed in Figure 14. The nails view 1400 is similar to the planets view shown in Figure 13. Each data item is represented by a shaft with a cap, having an appearance like that of a nail. In this view four company fundamentals are used to generate image data. The radius, distance and angle 1401, 1402 and 1403 are defined in the same way as the radius, distance and angle in the planets view 802, except that the radius relates to the radius of the cap of each shaft. An additional fourth company fundamental is used to control the height of the cap above the plane of the circular grid. This fourth fundamental is selected at data entry field 1404. As with the planets view, the rate of rotation of the circular grid, and all the objects displayed on it, can be adjusted. In both the planets view and the nails view, rotation may be paused by operation of the "p" key.

Figures 15 and 16

The particles view 804, indicated in Figure 8, is illustrated in Figure 15. In the particles view, data items are shown as having a size related to ten raised to the power of the market capitalisation spherical particles.

These are initially located randomly in a volume of 3D space. Each particle, representing a data item, has three contributing company fundamentals, defined in data entry fields 1501, 1502 and 1503 in the particles properties window 1504. Each fundamental has an associated factor, that determines its contribution to the behaviour of each data item. The particles view window 1505 shows a model in which three attractors 1511, 1512 and 1513 exert a gravitational force of attraction upon each data item. The attractors are red, blue and green in color. The magnitude of the attraction is dependent upon the inverse square law of gravitation, and upon the mass of the data item with respect to red, blue and green dimensions of gravity, as defined at fields 1501 , 1502 and 1503. Thus, for each company item, three gravitational forces are identified, with respect to each of the red 1511, blue 1512 and green 1513 attractors. A vector addition is performed, followed by an integration with respect to time, in order to determine a movement of a company item 1521 in 3D world space.

The model can be restarted by activation of a run model button 1522, which causes all particles to be randomly located. Dynamic or tertiary conditions may provide indications of market behaviour. After a period of time, some or all of the data items will have moved to a stationary position, or a condition of localised positional oscillation between two of the attractors. This is illustrated in Figure 16. During the simulation, it is possible for trajectories to take data items outside of the bounding volume of the model. As a data item, or particle, hits the inside of the bounding 3D volume, it is reflected, and thereby returned to a region of space where it may be influenced by the pull of one or all of the three attractors.

In the planets, nails and particles view the objects used to represent companies in 3D space may be color coded in accordance with their market sector. A typical colour scheme is as follows:

Basic Materials - Red Capital Goods - Olive Green Conglomerates - Light Green Consumer Cyclical - Dull Yellow Consumer Non-Cyclical - Light Purple Energy - Cyan Financial - Dark Grey

Healthcare - Light Grey Services - Dark Purple Technology - Gold Transportation - Dark Cyan Utilities - Orange

The provision of this uniform colour scheme across several switchable views enhances the coherency, quality and quantity of information created by browsing the data.

Figure 17

The top list view 805, indicated in Figure 8 is shown in Figure 17. The list view differs from the other views in that the user-defined data requirement 704, shown in Figure 7, defines the number of data items to display, but does not select them individually. Selection of individual data items is performed automatically during the process of generating image data. The user may, however, at step 704, define an overall collection of data items from which data items will be automatically selected. An example of this is a user definition of a requirement to select US stocks, shares and options.

In the top list view window 1701 a plurality of lists is displayed. Each list is sorted in accordance with a particular company fundamental or user- defined type. The same company appears at different positions in each list. By clicking on a data item 1702 in a particular list, the data item turns blue in colour. The same data item is highlighted by a blue colour 1703 in its position in other lists. However, because the user defines and thereby restricts the number of items in the lists, and the total number displayable is 30 items it is possible that the company will not appear in all of the lists that are shown in the window 1701. When a company is highlighted in this way, its details are displayed in the fundamentals window 1707. The user defines the number of items that appear in each list in the items dialog 1704 in the top list properties window 1705. The user adds or deletes fundamentals from a list of lists at 1706. From one to thirty-four lists can be displayed in the top list window 1701 , and this number is limited only by the clarity and resolution of the monitor 304. Similarly the limit on the number of list items 1704 is limited by clarity.

Each data item, represented by a column in each list in the top list view 1701 , is colour coded. A white column indicates that the position of the data item within the list has remained unchanged since the last data update. A green column indicates that the position of a data item is rising in the list. A red column indicates that a data item is decreasing in position in the list. A gold-coloured column indicates that the element has just appeared in the visible part of the list. Each column has a plus (+) or minus

(-) sign on its top surface. This indicates whether the absolute value of the data item is rising or falling.

Figure 18 The process 804 of generating the particles view shown in Figures

15 and 16 is detailed in Figure 18. At step 1801 all the particles are given random X, Y and Z positions within a bounded volume of three-dimensional world space. Their velocities, VX, VY and VZ are all set to zero. At step 1802 preparation is made for the next update. This includes evaluating elapsed time since the previous update, DT, and initialising the next data item for selection as being the first of the data items. At step 1803 the first or next data item is selected. Each data item is represented by a particle, whose movements are governed by red, blue and green gravitational attraction towards the red, blue and green attractors in the three dimensional world, respectively. These interactions are calculated at steps 1804, 1805 and 1806, respectively. Steps 1804, 1805 and 1806 result in the velocities VX, VY and VZ for the particle being modified. At step 1807 these velocities are each multiplied by DT to identify an incremental change in position for each of these dimensions, which are then added to the base X, Y and Z co-ordinates for the particle.

At step 1808 boundaries are checked. As a result of accelerations applied to a particle, it is possible for a trajectory to result in a particle being lost from the gravitational pull of the red, blue and green attractors. Since this would prevent any further useful interactions with the particle being observed, this condition is avoided by the boundary checking at step 1808. Each of the X, Y and Z values for the particle are checked against minimum and maximum values. This defines a bounding box in the three-dimensional world. If the position in any dimension is found to be outside this box, the corresponding velocity, in that dimension, is reversed and divided by ten.

At step 1809 friction is applied. This is achieved by multiplying the velocities VX, VY and VZ each by 0.999. At step 1810 the colour of the particle is defined by a financial characteristic. In the preferred embodiment this is the market sector that the data item belongs to, colour coded in accordance with the associations described previously. At step 1811 the particle is drawn at its new position.

At step 1812 a question is asked as to whether any more data items remain to be drawn as particles in the present update cycle. If so, control is directed back to step 1803, and the next data item is selected. Alternatively, if all particles have been drawn, control is directed to step 1813, where all other objects are drawn. This includes the red, blue and green attractors 1511, 1512 and 1513, the optional grid markings, in blue, and any associated text or other symbols that have been requested by the user. At step 1814 the particles view window 1505 is updated with the redrawn objects. Thereafter, control is directed to step 1802, and the next iteration of the gravitational model is performed. The steps 1802 to 1814 are repeated until an interrupt is generated from another parallel process selected by the operating system 601.

Figure 19

The steps for calculating red, blue or green attraction 1804, 1805 or 1806 are the same, and are summarised in Figure 19. At step 1901 the mass M of the currently selected data item is determined with respect to the red, green or blue attractor. The mass is given by the nth numerical field multiplied by its respective scaling factor. Thus, when calculating red attraction, M is given by the value of the first numerical field and its respective user-defined scaling factor. These are defined in data entry field

1501 shown in Figure 15. Numerical data entry fields 1502 and 1503 effectively define the mass M in the case of the blue and green attractors respectively.

At step 1902 the co-ordinates, X, Y and Z of the particle have the co- ordinates of the attractor subtracted from them, thereby resulting in coordinates AX, AY, AZ that define the position of the particle with respect to the currently considered attractor. Co-ordinates for the attractors, given that they lie in a plane at Z=0, are: RED X = -5, Y = -4

BLUE X = 0, Y = 4.66

GREEN X = 5, Y = -4

At step 1903 a radius R from the centre of the attractor to the centre of the particle is calculated as the square root of the sum of the squares of AX, AY and AZ. At step 1904 the law of gravitation is applied using mass M and radius R.

Figure 20

The step 1904 of applying the law of gravitation is detailed in Figure 20. At step 2001 a question is asked as to whether the value R, calculated at step 1903, is greater than 0.7. If so, this indicates that the particle and the attractor are sufficiently far apart for the ordinary laws of gravitation to apply. If they are close, then this is considered as resulting in reflection. The equations used to simulate gravity are applied at step 2002. The velocities VX, VY and VZ of the particle are updated in accordance with the inverse square law. At step 2003, these same velocities are updated using equations resulting in reflection from the attractor.

Claims

1. Data processing apparatus including memory means for storing instructions and data items, wherein image data is generated from a plurality of said data items, and processing means for generating said image data, wherein said instruction define operations to be performed by said processing means to generate said image data from said data items; said data items have at least one numerical field representing financial data and said data items are modelled as objects in a three dimensional world view; wherein said operations include an initial step of defining positions of said objects in said three dimensional world view; with subsequent steps applied to each of said objects of: defining a movement in response to an accelerating force identified with respect to said financial data and at least one other object; drawing the object in its new position; and a final step for all objects , of repeating said defining and drawing steps.

2. Apparatus according to claim 1, wherein said three dimensional world view includes at least one attractor object that is not a data item.

3. Apparatus according to claim 2, including multiple attractor objects that accelerate data items in response to values in respective numerical fields of financial data in said data items.

4. Apparatus according to claim 1 , wherein an accelerating force experienced by said object results from combining a plurality of accelerating forces that are generated with reference to a plurality of respective numerical fields in a said data item.

5. Apparatus according to claim 1, wherein said movement is calculated with reference to a user defined rate value.

6. Apparatus according to claim 1, wherein said data items are derived from data received via the internet.

7. Apparatus according to claim 1 , wherein data items are updated when they change.

8. Apparatus according to claim 1, wherein said objects are coloured in accordance with a financial characteristic of their respective data items.

9. Apparatus according to claim 8, wherein said financial characteristic is market sector.

10. Apparatus according to claim 1 , wherein said objects interact by reflection or collision.

11. A method of generating image data from a plurality of data items, in a data processing system including memory means for storing instructions and said data items, and processing means for generating said image data; wherein said instructions define operations to be performed by said processing means to generate said image data from said data items; said data items have at least one numerical field representing financial data and said data items are modelled as objects in a three- dimensional worid view; and said operations include an initial step of defining positions of said objects in said three-dimensional world view; with subsequent steps applied to each object of: defining a movement in response to an accelerating force identified with reference to said financial data and at least one other object; drawing the object in its new position; and a final step, for all objects, of repeating said defining and drawing steps.

12. A method according to claim 11, wherein said three- dimensional world view includes at least one attractor object that is not a data item.

13. A method according to claim 12, including multiple attractor objects that accelerate data items in response to values in respective numerical fields of financial data in said data items.

14. A method according to claim 11, wherein an accelerating force experienced by a said object results from combining a plurality of accelerating forces that are generated with reference to a plurality of respective numerical fields in a said data item.

15. A method according to claim 11, wherein said movement is calculated with reference to a user-defined rate value.

16. A method according to claim 11 , wherein said data items are derived from data received via the Internet.

17. A method according to claim 11 , wherein data items are updated when they change.

18. A method according to claim 11 , wherein said objects are coloured in accordance with a financial characteristic of their respective data items.

19. A method according to claim 18, wherein said financial characteristic is market sector.

20. A method according to claim 11 , wherein said objects interact by reflection on collision.

21. A computer system programmed to execute stored instructions such that in response to such instructions a system is configured to perform methods according to any of claims 11 to 20.

22. A computer readable medium having computer readable instructions executable by a computer such that, when executing said instructions, a computer will perform the method steps according to any of claims 11 to 20.

23. A computer readable memory system having a plurality of data fields stored therein representing a data structure, wherein said data structure facilitates the execution of a method according to any of claims 11 to 20.

24. In a computer system having a graphical user interface including a display and a user interface selection device, a method of providing and selecting from a menu on the display facilitated by method steps according to any of claims 11 to 20.

25. A method of generating image data from a plurality of data items substantially as herein described with reference to the accompanying

Figures.