Kick Off Meeting Island of San Servolo - Venice, Italy 11th to 13th February 2008

1. Kick Off MeetingIsland of San Servolo - Venice, Italy11th to 13th February 2008 WP13 – Cargo Intelligence Mitja Jermol, JSI

2. Agenda • WP13 • Aims and goals • Structure and timeline • Detailed Task description • T13.1 • T13.2 • T13.3 • T13.4 • Deliverables • Partners and MM distribution • Names and responsabilities • Organisational issues • D13.1 draft TOC

3. WP13

4. WP13 – Aims and goals The WP consist out of two major parts: • Acquisition and processing of real-time data • Data-mining for modeling, rule extraction, forecasting and anomaly and trend detection. Developing prototype services based on KT, DM, context/ambient technologies and ML that will support the CI. Enabling the local intelligence by means of agent based technologies. • define data sources that contain relevant information for the EURIDICE problem domain (internal, external), • data acquisition methods and tools, • data pre-processing methods and tools (data gathering, information extraction, data cleaning, data fusion), • methods and tools for intelligent data analysis (DM, TM, WM). • prototypes for • anomaly detection providing information to alerting module, • knowledge discovery that will provide input to formalised background knowledge database, • a prediction prototype and • early trend detection prototype, • Combined in EURIDICE Decision Support System. • Testing in selected and focused scenarios.

5. WP13 Structure and timeline Task 13.1: Data sources definition, Data acquisition and scenario selection (m1 – m11) Task 13.2: Data pre-processing methods and tools development (m1 – m18) Task 13.3: Intelligent central and distributed data analysis and knowledge discovery Task 13.4: Anomaly detection (m13 – m23) Task 13.5: Event prediction and early trend detection prototype (m18 – m30)

6. Layered structure and WP dependencies Joint meetings with other WPs WP13 T13.5 WP34 T13.3 T13.4 WP32 WP12 T13.2 WP33 T13.1 WP14 WP11 WP21 WP22

7. Task 13.1: Data sources definition, Data acquisition and scenario selection • m1-m11 • 58MM • Goals: • Define the Euridice problem space in terms of data available • Search for: • Internal data sources • WP12 predefined rules • External data sources • Analyse the data sources and perform tests • Define additional usage scenarios • D13.1 - Data sources definition and scenario selection (m11) R • Task leader JSI • Partners involved: Ins, VIU, Telit, Sing, Tredit, Enicma, Cetim, Logica, Oracle, FHV, CAEN • Related to: T12.1, T11.1, T14.1, T21.1, T22.1

8. Task 13.2: Data pre-processing methods and tools development • m1-m18 • 50MM • Goals: • developing data pre-processing tools • Data acquisition and data crawling, • Data cleaning, • Data fusion, • Internal format (Latent Semantic Indexing, Principal Component Analysis and similar). • Data storage for analytics • Develop set of modules to prepare data for online and offline analysis. • ID13.1 Data gathering, cleaning, fusion and pre-processing (month 18) • Task leader FHV • Partners involved: Ins, JSI, VTT, VIU, Telit, Sing, Enicma, Cetim, Logica, Oracle, FHV, CAEN • Related to: T12.1, T11.1, T21.1,

9. Task 13.3: Intelligent central and distributed data analysis and knowledge discovery • m13-m30 • 29 MM • Goals: • IDA tools (JSI, Oracle) like association extraction, summarization, classification and prediction, regression, clustering, pattern directed methods, OLAP, visualization techniques. • Providing rules to the formalised background knowledge database. • Developing agent based system based on open source platform (JADE, JAF, Cougaar etc), for decision making based on local information and the information that was fed back from the external resources. • D13.3 Data-analysis and knowledge discovery prototype (m29) P • Task leader VIU • Partners involved: Ins, JSI, VTT, VIU, Telit, Enicma, Oracle, FHV

10. Task 13.4: Anomaly detection • m13-m23 • 10MM • Goals: • finding anomalies in the complex data sets. • offline and online detection module • Data filtering (Kalman filter) • Pattern analysis, Special statistic methods (CUSUM, Hidden Markov Models) • Rule based methods • Clustering, Decision trees • Kernel methods, SVM, • Time series analysis, WSARE • … • D13.2 Anomalies detection prototype (m23) P • Task leader JSI • Partners involved: JSI

11. Task 13.5: Event prediction and early trend detection prototype • m18-m30 • 12MM • Goal: • develop the event prediction and early trend detection prototype based on the scenarios described in ID13.1. • combination of data-mining and time series analysis on event sequences. • Integrate prototypes in the EURIDICE Cargo intelligence system. • A sophisticated risk-management system at the local intelligence level • D13.4 Prediction and trend detection prototype (m29) P • Task leader JSI • Partners involved: JSI

12. Promised deliverables • ID13.1 Data gathering, cleaning, fusion and pre-processing (internal report) (month 18) R • D13.1 Data sources definition and scenario selection (month 11) R • D13.2 Anomalies detection prototype (month 23) P • D13.3 Data-analysis and knowledge discovery prototype (month 29) P • D13.4 Prediction and trend detection prototype (month 29) P

13. Partners and MM distribution Task leaders Partners with development power

14. Names and responsabilities

15. Organisational issues • Meetings • F2F meeting • First meeting soon • Skype – regular 14 days meetings • Further: • Development and software repository • Testing and bug tracking procedures • Deliverables peer review • To-do • Partner information, competences, software available (All) – 22.2. • To be sent to Mitja • Task level detailed plan (Task leaders) – 22.2. • To be sent to Mitja • D13.1 draft TOC with T13.1 responsibilities sent to T13.1 members - 21.3. (Mitja) • WP13 meeting ?

16. D13.1 TOC • Data sources definition and scenario selection (month 11) • Introduction • Data sources in relation to KT methods • Euridice internal data sources analysis • External data sources analysis – public sources • Overall contextual space • Potential use case scenarios

17. Contact details • Mitja,jermol@ijs.si • Mobile : +386 41 765034 • Phone: +386 1 4773593 • Skype handler: mitja38 • Messenger: mitja.jermol • Facebook, Linkedin