Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia

Professor, D.Sc. (Tech.) Eugene Kovshov MSTU «STANKIN», Moscow, Russia

As of today, the issue of Big Data processing is still of high importance. Data flow is increasingly growing. Processing methods are outdated and have not kept pace with the growth. Methods used today are not good enough for finding fundamental solutions! Methods do not allow the accumulation of stored information for subsequent processing quality. 500 400 300 200 100 0 Data flow processing 1 3 5 7 9 11 13 15 17 19 21 23 Opportunity to data processing Conclusion: A more objective system of storage and knowledge management is needed, which would exceed human abilities, thus taking into account the expert subject knowledge and able to work with the increasing flow of data in real time. Data accumulation DB Storage only Processing Data flow Finding solutions is carried out mainly in the perimeter of specialized institutions. 5 However, the scientific field is much wider, and in order to find solution, one needs to go beyond the established framework. 3

Innovation of MIVAR approach ( Professor, D.Sc. (Tech.) Oleg Varlamov (BMSTU), 2002) is in utilization of multidimensional databases and inference with linear complexity. Hence, a high-speed processing of large amounts of data becomes available and systems work in real time. 1) MIVAR technology of information accumulation is a method of creating of global evolutional bases of data and rules (knowledge) with changeable structure based on the adaptive discrete MIVAR information space of unified representation of data and rules which bases on three main definitions: Thing, Property, Relation. 2) New MIVAR technology of information processing - is a method of creation of the system of logic inference or automatic construction of algorithms from modules, services and procedures based on the active MIVAR net of rules with lineal computational complexity. MIVAR approach unifies and develops production systems, ontology, semantic nets, service-oriented architectures, multi-agent systems and other modern information technologies. Results of experiments (time of solution from the number of objects and rules) 3,5 million rules on PC 4

Statistics Graphs Trees Cognitive maps Ontologies ER-diagrams UML There is an information technology that allows to combine advantages of approaches used in information and knowledge processing. MIVAR approach Important note! This approach allows the use of existing knowledge, but formalization is still needed. 5

MIVAR approach can combine data storage, processing and logical inference. We ll use a multidimensional space of mivar nodes and their connection vectors. Each mivar node there can be described as: V as an object; S as property; R as relation; Z as value; T as time. Nodes: {<V1, n1>, <V2, n2>,..., <Sm, mb>,..., <Ok, kc>, <Z>, <T>, <K>} Vectors: {<Vх1, n1>,..., <Vхi, ni>, <Ok, kc>, <Vy1, m1>,..., <Vyj, mj>, Z, T, K}. Multidimensional here means that we have an individual dimension for each characteristic. We have no limits in number of dimension and can easily add new, as well as new nodes and vectors. We distinguish three main steps of MIVAR information processing: 1) formation of a multidimensional evolutionary databases of MIVAR data and MIVAR rules, which accumulate information in the form of "object, property, relation"; 2) working with the DB and the construction of an algorithm for solving a given problem; 3) upon receipt of the algorithm execution of all the calculations and find an answer. MIVAR = Multidimensional Informational Variable Adaptive Reality 6

Technological platform (TP), based on MIVAR approach, was created. The goal of this platform is to create innovative products: intelligent systems capable to solve complex logical problems in a real-time mode using large databases (Big Data). The principal difference from the existing products Lack of traditional hard (pre-built) algorithms The system will automatically generate the algorithms for the solution of existing knowledge. Exception: For correct operation of the products you need to detail description of the cause-effect relationships (links) in the solutions of the problems (rules like: "if... then...»). 8

MIVAR information systems - the most efficient replacement of human machine. We got the formula of the ideal expert: maximum possible knowledge about the subject + experience + absolute memory + speed of thought Various information systems, based on MIVAR approach, can be developed : Expert systems Control process system Decision system support Diagnostic systems System modeling and design, etc. 9

Prarmeters Rules Relations Statistics Priorities Universal subject area model Checkpoints Model types Systemicstructural Logical conclusion (design algorithm for solving and perform solution ) Structural statistics Statistical 10

Only at the stage where subject matter is initially designed, intensive experts involvement is required to define, formalize and match relevant knowledge Model creation Experts Evolutionary model Multidimensional database Expert subject knowledge Existing knowledge Rules & relations Structure & content Instructions & conditions Textbooks & reference books 11

Input data Data Monitoring 24/7/365 Situation assessment Visualization Data DB System Forecast Decision support system (User or ICS) Data Data Input data Decision map + rationale Data request/ control 12

Various information systems, based on MIVAR approach, can be developed: Expert systems Control process systems Decision system support Diagnostic systems System modeling and design, etc. Important note: The system is more complex, the greater the gain of MIVAR approach! 13

External Data (structured) Interfaces Result API (external) Load Data (in SDK) Input Internal Data SDK / UI (Model Maker) Output service (rules, relations, parameters, etc) Data Base (SQL) Output Internal Data Output service (result) MIVAR Logic (new) Input Data (for Auto Process) Input Data (for Auto Process) 14

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We re working under image recognition system that operates on a completely new level, according to: Smart recognition. Getting the most complete and accurate text description of the image, including working at the level of context thanks to MIVAR knowledge base. Smart search. Thanks to using mivar bases of data and rules (knowledge) with changeable structure based on the adaptive discrete MIVAR information space of unified representation of data and rules. 17

Knowledge base MIVAR-3D MIVAR-text Cloud of sence 18

Image Segmentation module Primary recognition module Feature extraction module Graph construction module VSO Knowledge base KB module access Removal context uncertainty module Testing for resistance Feedback module graph VSO (visualization) Stable scenario 19

Image awareness using context Context MIVAR Hence, not just description (understanding) of the actual objects would be more precise, but also there relations with other objects Initial "eyes" for identification and classification (recognition) of objects System relations Part Whole Spatial relations: Overlapping (z-index), Proportions of an object Remoteness, Position, The mutual arrangement of objects, The proportions between the objects. Real size of the object (knowing the camera settings) Color Texture Shape Contour MIVAR Current solutions on the market 20

Initial recognition property coach Graph VSO grey left left right basketba ll right ball lock er up property Property up under right under lock er lock er property grey grey property red Pointed recognition 21

Parameter Features Prototype development File type Synthetic RGB images of objects and scenes Real 2D scene, video, 3D scene Input data console, web camera, kinect User interface,different camera types, sensors Initial recognition Marks Segmentation Dictionary Smart search Smart recognition 10 categories Color, texture, spatial relations, form 2 segmenters (ours + Kinect) Dividing images into 50 categories of objects Visualization of some of the concepts in the text Recognition of synthetic images with a small number of categories (10) with transition to the pattern VSO Recognition up to the number of objects in 'text' dictionary (2134 objects), improving the accuracy of recognition, creation an image hierarchy Screenwriting, part-whole, posture, matching objects with action, motion Improving the accuracy and speed of segmentation Using text dictionary (2134 objects) Rendering text requests Recognition of real images with a big number of categories with transition to the pattern VSO 22

Smart search and smart image recognition in social networks, Internet etc. Medical image processing Intelligent (MIVAR) security systems Video surveillance systems Unmanned vehicles Robotics 23

RECOGNITION OF DYNAMIC OBJECTS 3D RECONSTRUCTION RECOGNITION OF STATIC OBJECTS MACHINE VISION Recognition of traffic lights Recognition of road signs 24