Context-aware data access models (Declaration of Intent Draft) Dmitry Namiot dnamiot@gmail.com Lomonosov Moscow State University Proposal: SkTech.RC/IT/Madnick

Contact info Email: dnamiot@gmail.com Phone: +7-495-939-23-59 Address: Russia, 119991, Moscow, Leninskie Gory, MSU, Faculty of Computational Mathematics and Cybernetics 2nd educational building, room 360

Position & educationation Position: senior scientist of Faculty of Computational Mathematics and Cybernetics, Open Information Systems Lab Educational background: Applied Mathematics (B.S. and M.S.) – Moscow Aviation Institute, Ph.D in Computer Science – Lomonosov Moscow State University

Research areas and past projects discrete simulation and statistical methods, compilation, grammars, domain specific languages knowledge management, logical chains, production systems, artificial intelligence, expert systems, real time operational systems, distributed systems: CORBA, then EJB, telecom development: open interfaces for telco (Parlay etc.), telecom protocols and services, web programming APIs and internet applications, location based systems and geo programming, distributed databases (Hadoop etc.), web services and semantic web, data mining, data curation in social networks

Accomplishments and recognition Author or co-author of over 60 journal articles and 4 books. Innovation Award at World Wide Java Cards Development contest (3GSM World), Best on Technology Award at World Wide Java Cards Development contest (3GSM World), several readers Choice Awards from computer magazines, several Java Developers challenges awards

Leadership and collaboration participates in European research projects (together with Rigas Technical University and Ventspils University College - Latvia), prepares and provides educational for European telecom firms (Iskratel, Slovenia), reviewer for several international conferences (ICST, IARIA), co-founder (as technical director) of several high-tech firms MSU teaching: database programming, Java programming for Internet applications.

Intentions for R&D Theme: “BIG DATA”: large-scale data gathering & mining Research issue: data mining services that let define context-aware actions for delivering (discovering) data to mobile subscribers Context-aware data access, context aware browsing

Intentions for R&D Nowadays mobile phones are becoming the primary source for possible data collections. “phone as a sensor” concept It is the typical example of schema-less big data. For example: environmental sensing and behavioral.

Intentions for R&D What kind of information snippets could be shown (delivered) for mobile subscribers based on various metrics that could be introduced for that vast amount of data?. The goal: provide a set of tools that let define (develop) some actions/triggers (e.g. delivering information to mobile phone) depending on the collected context data in the real time. In general it leads to building richer and more personalized mobile experiences.

Intentions for R&D Elements are (at least): data collection (gathering) modules data persistence mechanisms new metrics for collected data (e.g. proximity as a service, fuzzy logic for data estimation etc.) developers API for using collected data in applications

Example: Spot Expert Collected data: Wi-Fi networks info Metric: Wi-Fi proximity Result: context-aware browser where available content if defined by the proximity rules Big data processing for the next steps: collect more sensing data, analyze data for several subscribers, add more metrics

Relevancy This project addresses the following hot areas in computing: M2M applications, mobile computing in the real word context-aware (ubiquitous) computing.

Novel & scope Context-aware computing for mobile devices is highly fragmented. The amount of practical applications is very low. There are no (almost no) development tools that cover context-aware applications. It covers multiple research areas: mobile OS and SDK, big data stores for data persistence, real-time analysis for big data, modern programming development tools and APIs, telecom standards.

Entrepreneurially promising Areas for the possible commercialization: Smart Cities projects distributing hyper-local news data to mobile subscribers (e.g. commercial info in malls, news data in campuses and office centers), real world games

Education Educational courses that could be provided in the connection with this project: mobile OS, mobile SDK, NoSQL databases, data patterns recognition, big data processing. We can develop new multi-disciplinary core courses for sensing data analysis. These courses will serve also as a basic point for PhD students