Abstract

ABSTRACT To perform visualization on mobile devices different types of data may use like text, picture, maps, physical objects, abstract data etc. According to data visualization is categorized in two areas of visualization that is, scientific visualization and information visualization. Scientific visualization refers to some specific type of data like physical data and it is used for computer modeling and simulation. Information visualization refers to abstract data and used in business and finance, administration, digital media and other abstract concepts. The physical and abstract data is only one classification but there are others classification like static and dynamic data, structured and unstructured data, or hierarchical and non-hierarchical data classification. This paper is focus on information visualization of abstract data on mobile devices. Keywords: Information visualization, abstract data, mobile devices. INTRODUCTION As per known information, mobile devices have so many limitations as compared to desktop computers like displays are very limited, the width/height ratio is very different from the usual, the on-board hardware is much less powerful; the input peripherals are often insufficient for complex tasks, the input techniques are different, connectivity is slower, affecting the interactivity of applications when a significant quantity of data is stored on remote databases, there is a lack of powerful, high-level graphics libraries. But Recent mobile are more powerful having high resolution, more colors, large screen, fast connectivity, various tools than old one, but still visualization on mobile devices has remained a challenging task Various types of visualization on mobile devices refer to various types of data. Information visualization is one of the visualization types that refer to abstract data. Abstract data is generally used in business and finance, administration, digital media and other abstract concepts. As the information is increasing day to day but mobile devices reduces in size. So visualizing large data on mobile is one of the challenges. INFORMATION VISUALIZATION AND ABSTRACT DATA [11]. Author [17] discus the different interface approaches for focus and contextual view i.e. overview-details, zooming, focus-context for desktop applications. To perform vehicle navigation on mobile devices different techniques has been used that are spatial information filtering, modeling the context and also use one adaptive visualization method. It perform various task i.e. navigation, search etc. Adoptive visualization is one of the new approaches, which consider context and influence aspects Information visualization technique having insightful design principles and effective visualization prototype can also be used to visualize semantic structure of classical music and get insight into musical structure Visualization of places and areas where the user's data is tagged using placegram which is diagrammatic map representation on the basis of cognitive map theories is present in Authors [15] present existing visualization techniques that hold multi resolution functionality and describe the visual hierarchical aggregation methods for massive data abstraction. Also presents interaction technique appropriate for multi scale visualization navigation. For the visualization of multi dimensional data there is use of cross filter visualization technique and also represents design plan for corresponding multiple view interface using cross filter Interactive Weather Information System or IWIS is developed using unique design-oriented visual images to represent a select group of weather information for the learners Glaze is one of the visualization structure based on GPS on mobile devices to see the structure and orientation of surrounding and navigates in environment To improve emergency response and situational awareness MobiVis, a visual analytics tool have been created, which integrates the idea of presenting social and spatial information in one heterogeneous network Another application of information visualization is for mobile learning in which authors INFORMATION VISUALIZATION TECHNIQUES FOR ABSTRACT DATA ON MOBILE DEVICES Hierarchical information visualization i.e. RELT (Radial Edgeless Tree) Hierarchical information visualization that is RELT (Radial Edgeless Tree), combines the features of both connection, and enclosure approach. RELT is one of the methods, which overcomes the drawback of connection and enclosure approaches. RELT uses adjacency and direction to represent relationships between nodes and visualized information in a radial layout The current music classification and selection is one of the applications of RELT. Representation of music files in 2D form which Cascade menus across multiple screens. The 3D type is simply a simulated 3D view with realistic looking albums. RELT allows the user to define the number of levels to be viewed. K-Means clustering: Two forms of visualization have been designed to represent stock values. The first one is devoted to visualization of different values at a given time; the second one is devoted to visualization of the evolution of such values with time. If the values are too numerous, then a clustering can be used to summarize them like a K-Means clustering to merge 40 stocks in 7 clusters. Clustering may be used to represent the variations in stock values in the form of bar chart. In which positive value are represented from bottom to top on the bottom line and negative value from top to bottom on the upper line. Pixel bar chart may used to visualize the advancement of values during a period for different clusters (or stocks). Fig. 2. Pixel Bar Chart. Each column present to a cluster and the number of stocks in the cluster relates to its width. Each daily value is mapped on a color scale. The different days of the period are represented from bottom to top Cluster visualization is given for the time series data in which clusters are used on calendars which combines with patterns shown as graph Clustering is also used to show the relation between interview and survey on mobile device in project management Ghost technique with push based mechanism: Real-time data is obtained from sensors such as GPS (Geographical Positioning System) and cameras that reorganized in few seconds. Recent updates of information correspond to their background information of past, so that in-field responders can follow changes over time. As the mobile devices have less memory so it is necessary to balance history store with actual memory. To visualize such data, ghost technique is used. Push based mechanism is also effective for the visualization of temporal and spatial historical data Information visualization technique with intuitive navigate: The visualization of patient data at temporal granularity shares some common interface features like time line, temporal scroll bar and zoom buttons. This technique helps the visualization on mobile devices with intuitive navigation [8]. Ghost view illustrative technique: Ghost view can be used for volume visualization or 3D multimedia data. Firstly tree cube structure visualization technique is used for the management of 3D multimedia data. Tree cube is generally hierarchical visualization technique, which is also known as tree map. Tree cube holds the 3D multimedia data like 3D geometry model. One of the objects of this model may be one model. And the relationship between these objects is referees as hierarchical which allows fast navigation. For the tree cube visualization small phylogenetic data and sub tree has been used. Like ghost view spring model is another technique, which generally used to see the division of the whole information space according to its attributes or the expansion of information objects. It also allows comparison between single objects and volume. So this technique is generally useful for multivariate data sets. Fisheye view with focus and context method: Focus and context visualization technique is more useful commonly used technique for information Magic eye view is generally modified for hierarchies on hemisphere. Every node in the hierarchy are mapped on the two dimensional Cartesian plane with two angles. M= 2 *x/maxx , T= 2 *y/maxy. Hemispheres are then used to calculate the Cartesian coordinates: x = r * sin(M)*sin(T), y = r*cos(M)*sin(T), z = r * cos(T) But there is a problem with magic eye view visualization for hierarchy because it displays lot of unused space on mobile screen and the node labels gives the parts of the presentation. Therefore magic eye technique has been improved which specify half ellipsoid or half spheroid with different coordinates. A spheroid is expressed by the formula: Adoptive visualization: Adoptive visualization is one of the new approaches, which consider context and influence aspects. Therefore it obtain different context like user location, time, environment, system information etc. After obtaining context on mobile devices then it sends to the server for additional processing. After that context classifying and dividing their properties on the basis of ontology have created database. This process is known as context modeling. Mapping tables has been created between context and GR (Geographic Relevance), which is a relation between a geographic information need and the spatio-temporal expression. Now on the basis of GR rules spatial information has been filter to find useful user-related information. Appropriate adoptive visualization technique has been used to find visualization-based context. And at the last visualized context has been present on mobile device Adoptive method is also used for the cryptographic visualization of geographic information on mobile devices Layer braid and theme fabric visualization prototype: Musical structure data contains different layers and themes those are getting back from descriptive essay. For the visualization of semantic structure of music data there are consistent, intuitive, effective and aesthetic design principles for visual representation. To represents layers in visualization there is use of different color plan. Prototype visualization those are layer braid and theme fabric provides micro level layer relationship and theme disparity and also provides interaction between layers and theme Diagrammatic map based visualization: Placegram is one of the diagrammatic map based visualization technique which includes preprocessing, place layout, connecting and weighting places or area clustering and label layout. Preprocessing is for input data (place) to merge them together for next steps. Here is use of place alignment algorithm to compress layout horizontally and vertically and in grid alignment places are break to brows using 4D keys and to get easy visual layout on mobile devices than normal mode. These places are link together by constructing minimum spanning tree and the corners of tree are flattening diagonally. Every place has almost one connection to any 4 sides. Place having more data and frequent visits are shown with large size [14]. Hierarchical Aggregation Visualization: In hierarchical aggregation it is needed to differ between overlapping and space filling visualization techniques that is overlapping never restrict on visual items layout while the space filling restrict on layout to avoid overlapping. These techniques provide visual representation of actual as well as aggregate data items. Hierarchical visual aggregation of scatter plot uses overlapping technique so there is occurrence of occlusion. There are above, below, level and range rendering traversals to travel visual hierarchical aggregation. For navigation and manipulation there are zoom and pan, drill-down and roll-up, local aggregation, flipping, coupled zooming and drilling interaction techniques. Like scatter plot there is also the visualization of parallel coordinates, star glyphs, tree maps, node link diagrams etc For the hierarchical visualization and interaction of social photos Radial Visualization: Radial visualization technique is useful for the description of interactive system who arranges the data in elliptical form. Some visualization techniques like pie charts, star plot, and radar plot are generally used in business and media to communicate numeric Relational entity resolution with novel network visualization: For the interactive entity resolution in relational data authors Visual analytic method: A visual analytic method has been developed on mobile devices to get crisis response of sensor and other types of data. [32] Uses client server approach, server for the conversion of sensor and other types of data, client for 2D/3D visualization and interaction. Client also uses circular queue for the organization of different types of data. To visualize data on mobile various types of categories are used i.e static, dynamic temporal, spatial, aggregation etc. Using interactive visual presentation of information a faster and better insight from data is possible. Avin Pattath and group Structured information visualization: Author [35] discuss two contents i.e. non-visualized and visualized content via graphic, animation, and image to compare and finds the result that structured information visualization is more effective for mobile learning. USE OF INFORMATION VISUALIZATION As compare to the desktop, mobile devices have many limitations but two main limitations are small screen and less memory. Due to the small screen size effective information display is difficult. So many information visualization algorithms and techniques of desktop like clustering, filter, focus and context etc may apply on mobile devices to solve mobile limitation. Information visualization allows effective visualization of large information on mobile devices. As the mobile devices have less memory, sometime it may takes the use of remote server for information storage. Therefore some information visualization techniques are used at server side and some at client i.e. mobile side. Client server approach is more convenient for the visualization of large amount of information from remote side on mobile devices. ANALYSIS AND DISCUSSION Here the various types of abstract data and their related visualization techniques have been arrange in tabular form. Abstract data types Information visualization techniques