ALPCHECK. WP 6 - Information System: Design and Implementation



Similar documents
AlpCheck Project an overview. Le Tecnologie satellitari per l infomobilità e i servizi al territorio. Il progetto Alpcheck

SAS BI Course Content; Introduction to DWH / BI Concepts

Talend Metadata Manager. Reduce Risk and Friction in your Information Supply Chain

POLAR IT SERVICES. Business Intelligence Project Methodology

8. Business Intelligence Reference Architectures and Patterns

THE QUALITY OF DATA AND METADATA IN A DATAWAREHOUSE

Data Warehouse: Introduction

Reduce and manage operating costs and improve efficiency. Support better business decisions based on availability of real-time information

Toward the Logistic System Slala Experience. Nicola Bassi

BIG DATA FOR MODELLING 2.0

Course DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

OPEN CALL FOR TENDERS No MARE/2014/14. Appendix 2. EUMOFA IT architecture and design

Retail POS Data Analytics Using MS Bi Tools. Business Intelligence White Paper

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Short notes on webpage programming languages

Enterprise Information Integration (EII) A Technical Ally of EAI and ETL Author Bipin Chandra Joshi Integration Architect Infosys Technologies Ltd

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing

Data Export User Guide

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Foundations of Business Intelligence: Databases and Information Management

SAP BusinessObjects Business Intelligence (BI) platform Document Version: 4.1, Support Package Report Conversion Tool Guide

Cloud-Based Self Service Analytics

Data Analytics and Reporting in Toll Management and Supervision System Case study Bosnia and Herzegovina

DATA MINING TOOL FOR INTEGRATED COMPLAINT MANAGEMENT SYSTEM WEKA 3.6.7

A Scalable Data Transformation Framework using the Hadoop Ecosystem

NOS for Data Analysis (802) September 2014 V1.3

Dimodelo Solutions Data Warehousing and Business Intelligence Concepts

Using MATSim for Public Transport Analysis

Data Warehousing Concepts

Permanent GNSS station network and AlpCheck: freight transport monitoring

Course MIS. Foundations of Business Intelligence

Seeing by Degrees: Programming Visualization From Sensor Networks

Eurotunnel 2020 potential traffic estimate (Extract)

Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot

Zurich Process. Incident Management in the Alpine Area For the attention of the Ministers of Transport of the Zurich Process

IDCORP Business Intelligence. Know More, Analyze Better, Decide Wiser

Hamilton Truck Route Study

Addressing Risk Data Aggregation and Risk Reporting Ben Sharma, CEO. Big Data Everywhere Conference, NYC November 2015

CUSTOMER Presentation of SAP Predictive Analytics

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

5.5 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall. Figure 5-2

PowerDesigner WarehouseArchitect The Model for Data Warehousing Solutions. A Technical Whitepaper from Sybase, Inc.

IST722 Data Warehousing

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING

D5.3.2b Automatic Rigorous Testing Components

Information Integration for Improved City Construction Supervision

Methods and Technologies for Business Process Monitoring

Technology-Driven Demand and e- Customer Relationship Management e-crm

Oracle Siebel Marketing and Oracle B2B Cross- Channel Marketing Integration Guide ORACLE WHITE PAPER AUGUST 2014

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc.

Construction Planning, Equipment and Methods ENGI 8749 Fall Semester, 2008 Tutorial #2 Resource Leveling using MS Project

Oracle Warehouse Builder 10g

Uploading Ad Cost, Clicks and Impressions to Google Analytics

A Strategy for Managing Freight Forecasting Data Resources

SQL Server Integration Services Using Visual Studio 2005

USE OF STATE FLEET VEHICLE GPS DATA FOR TRAVEL TIME ANALYSIS

Interfacing SAS Software, Excel, and the Intranet without SAS/Intrnet TM Software or SAS Software for the Personal Computer

LearnFromGuru Polish your knowledge

Business Intelligence. A Presentation of the Current Lead Solutions and a Comparative Analysis of the Main Providers

DATA WAREHOUSE STAT.UNIDO.ORG

Ofgem Carbon Savings Community Obligation (CSCO) Eligibility System

SSIS Training: Introduction to SQL Server Integration Services Duration: 3 days

雲 端 運 算 願 景 與 實 現 馬 維 英 博 士 微 軟 亞 洲 研 究 院 常 務 副 院 長

Whitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE

Quick Start. Creating a Scoring Application. RStat. Based on a Decision Tree Model

Testing Trends in Data Warehouse

Sisense. Product Highlights.

Fleet Manager Quick Guide (Non Maintenance Mode)

Contents WEKA Microsoft SQL Database

SAP Data Services 4.X. An Enterprise Information management Solution

Fluency With Information Technology CSE100/IMT100

FHWA Office of Operations (R&D) RDE Release 3.0 Potential Enhancements. 26 March 2014

By Makesh Kannaiyan 8/27/2011 1

BI Requirements Checklist

Instant Data Warehousing with SAP data

The Role of the BI Competency Center in Maximizing Organizational Performance

Chapter 6 FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Learning Objectives

2. Metadata Modeling Best Practices with Cognos Framework Manager

Databases in Organizations

IT-Pruefungen.de. Hochwertige Qualität, neueste Prüfungsunterlagen.

Understanding Data Warehousing. [by Alex Kriegel]

An Architectural Review Of Integrating MicroStrategy With SAP BW

TIBCO Spotfire Business Author Essentials Quick Reference Guide. Table of contents:

Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities

IMPLEMENTATION OF DATA WAREHOUSE SAP BW IN THE PRODUCTION COMPANY. Maria Kowal, Galina Setlak

Quality Assurance for Hydrometric Network Data as a Basis for Integrated River Basin Management

Business Intelligence for SUPRA. WHITE PAPER Cincom In-depth Analysis and Review

Structure of the presentation

SAS Institute Exam A SAS BI Content Developmentfor SAS 9 Version: 7.0 [ Total Questions: 107 ]

ER/Studio Enterprise Portal User Guide

COMHAIRLE NÁISIÚNTA NA NATIONAL COUNCIL FOR VOCATIONAL AWARDS PILOT. Consultative Draft Module Descriptor. Relational Database

Establish and maintain Center of Excellence (CoE) around Data Architecture

Task CMU Report 06: Programs for Design Analysis Support and Simulation Integration. Department of Energy Award # EE

SQL Server Administrator Introduction - 3 Days Objectives

IAF Business Intelligence Solutions Make the Most of Your Business Intelligence. White Paper November 2002

PSS SINCAL - Overview -

ElegantJ BI. White Paper. Considering the Alternatives Business Intelligence Solutions vs. Spreadsheets

OpenEmbeDD basic demo

A universal method to create complex reports in Excel

Building Views and Charts in Requests Introduction to Answers views and charts Creating and editing charts Performing common view tasks

Transcription:

ALPCHECK WP 6 - Information System: Design and Implementation Authors:Ing. Ernesto della Sala (NETHUN) Ing. Eugenio Morello (CSST) Ing. Michael Schedl (PARADIGMA)

WP6-Objective Statement from ALPCHECK WP5: AlpCheck is undertaking the ambitious task of initiating a process which should eventually lead to the establishment of an informative system for transport through Alpine area. Such an informative system will only be created through close co-operation of all Alpine countries and the European Commission. WP6 design and implementation aims to: Design system Road map system architecture technology (peripheral, central) Software (central) Develop system Implement (pilot projects)

WP6 road map Road map (Datawarehouse DWH) First step (data base) Data transformation extraction from feeder systems (ad hoc interface) Transformation / normalisation Data preparation and storage Data interpretation and analysis (query and analysis) Intermediate step (Decision Support System) Data interpretation and analysis (+ traffic model) Data presentation (georeferenced user interface) Final step (Infomobility) Dynamic acquisition of traffic data (including real time events) ALPCHECK ALPCHECK

WP6 road map Road map First step (data base) Collect traffic data from different sources From data to information (Harmonize, normalize) Information manipulation (query and analysis easy and flexible) Data presentation (reporting) Business intelligence tools+web application

WP6 road map Road map intermediate step (DSS) Build transport models chain Offer (road network) model Demand model (OD matrix time dependent) Behavioural model (modal split and assignment DUE, SUE, etc) Georeferenced GUI Model platform (dynamic)

WP6 road map Road map Final step (infomobility) Build tool working in real time collecting traffic data and events, making forecast on traffic evolution, Generating suitable information for road users Web interface Traffic Management & infomobility platform

WP6 Develop System (I) Datawarehouse DWH (APV) Selection of Tool Super Star Training course Selection of sources Traffic data so far identified Pilot projects Sources interfaces Transformation / normalisation Web application (CSST) Design Traffic data model GUI interface development

System architecture Origin DBs ETL Staging Area (RDBMS) Super Channel Metadata Web GUI SUPER STAR SuperWEB Google Maps WebServices SuperCROSS

WP6 data normalisation Data to be normalised Road Traffic (section, stretch) Flow Speed/Travel time Vehicle classification OD (zone) Intensity Type Passengers Freight Pilot project

WP6 data normalisation normalisation Have common view of data from different sources Suitable organisation of data including support data = DB tables Critical issue in normalisation: Time resolution Vehicle classification Freight classification Approach: Minimum common among data Minimum level of details + common group

WP6 data normalisation Tables Static tables: Source information Data owner Type of Vehicle classification Type of freight classification Time resolution Data collection method Traffic data Section/stretch list (anagraphic info) Road list Calendar Section group (user defined) OD data Zone list (NUTS, other) Relation category (passenger, freight, vehicle/freight classification)

WP6 data normalisation Tables Dynamic table Traffic data (5min/hour Resolution, per direction) Flow (per vehicle category) Speed (Harmonic average) Type of data (measured, estimated) Accuracy OD data (hour/day resolution) Flow (passenger/freight) accuracy

WP6 common Data model

WP6 ETL and load process design ETL routines Flat Files (csv, xls, etc.) Staging Area (rel. DB) Import SuperCHANNEL Stand-alone DBs (SuperSTAR) ETL routines Common Model (rel. DB) Import Super CHANNEL Common DB (SuperSTAR)

data sources status Flat Files (csv, xls, etc.) Cross Alpine Freight-Transport/BMVIT ASFINAG Frejus tunnel Mont Blanc Gran San Bernardo Autostrade per l'italia Veneto Region Permanent automatic counting stations in Germany Ventimiglia Slovenia Pilot Project Venice/Stuttgart Pilot IREALP - SLALA Pilot Project Vienna Pilot Project Val d'aosta Pilot loaded loaded loaded loaded loaded loaded work in progress loaded loaded loaded work in progress work in progress no data no data

data sources Flat Files (csv, xls, etc.) Flat files different formats (csv, MS Access, MS Excel) Mostly well-documented Some improvement in documentation possible

Staging Area Staging Area (rel. DB) One DB for each data source Intermediate storage for source data Starting point for: Loading the Common Design Generating separate SuperSTAR databases Relational databases

SuperSTAR databases Stand-alone DBs (SuperSTAR) One DB for each data source User-accessible databases Used to create reports for standalone data sources Registered with SuperSTAR server

Common Model Common Model (rel. DB) One DB design combining the different data sources in the dimensions Time Location (counting stations, O/D) Vehicle type Intermediate storage for common SuperSTAR DB Relational database

Common Database (SuperSTAR( SuperSTAR) Common DB (SuperSTAR) User-accessible database containing the common model Used to create reports across and combining several data sources Registered with SuperSTAR server

Common Database (SuperSTAR( SuperSTAR) Common DB (SuperSTAR) Estimated goods distribution Brenner 2004-06

Import process (I) Import SuperCHANNEL Relational source database SuperSTAR target database Definition of facts (counting variables) Definition of classifications (category variables) Defined with SuperCHANNEL

Import process (II) Import SuperCHANNEL

ETL process ETL routines Relational source database or flat files Relational target database Extraction of data from source format (file formats, DB design) Transformation in target design (standalone, common designs) Loading of data to the target Defined with KETTLE

WP6 Web application Objective: Develop a tool which allow user to see (on a map) and offer possibility to extract (download) normalised data from DWH using Internet. Main requirements: Data have to be georeferenced (suitable map) user profile: Very simple representation of data, few interaction with the system

ain characteristics of the web GUI database (1/2 Microsoft Access format (possibly converted to db2 format) ready for encompassing any type of traffic information databases 18 tables hosting information on: georeferenced OD relations and traffic measures at different time steps sections of measurement vehicle types and classifications load types measurement campaigns and responsible entities road types and classifications feeded through dedicated procedures for data conversion from Alpcheck DWH (according to the fields correspondence resulted from the orgin DBs analysis)

ain characteristics of the web GUI database (2/2 May 2008

Main characteristics of the web GUI (1/3) Web application with map interface based on Google Maps (Javascript - PHP) Data extraction based on combo boxes and "cascade" selections Four data families: day-based traffic year-based traffic ADT along years OD matrices (NUTS0 to NUTS3 level based)

Main characteristics of the web GUI (2/3) Data families (one sheet each) Map interface Selection boxes

Main characteristics of the web GUI (3/3) Selectable parameters: measurement site data origin (measurements campaign) day type (working day / holiday, etc.) day of the week vehicle type (aggregated according two macro types light and heavy vehicles) loadtype(whereavailable) Only the choices with available data are proposed to the user, avoinding no data results from the query Results request can be launched at any stage of the selection

Outpu (1/3) The results of the data inquiry are shown to the user in form of: tables graphs map-based representations The result data series are disaggregated according to the available combinations of the selectable parameters The dimension of the results (number of time series, rows and columns of the OD matrices) varies according to selection stage at which the inquiry is launched

Output (2/3) esults graph Number of time series esults table Time series of all combinations of the parameters

Output (3/3) Results map Results table (OD matrix)

Selection example: day-based traffic (vehicles per hour) Results window selection by: - section and direction - data origin (campaign) - vehicle and load type selection summary one time series and diagram for each year (and possibly each vehicle and load type) Selection window

Selection example: : OD Matrices Results window lection by: ection ata origin (campaign) ehicle and load type rigin and destination zone gh versatility for NUTS el choice) selection summary Maprepresentationof total origins (red) and total destinations (blue) per zone Selection window May 2008 OD relations according to selected NUTS level

WP6 Web Application - Remarks a. The selection scheme is thought as a cascade selection, where the selection options for a certain category (scroll-down window) include only those for which the dat base effectively contains data. This assumes that for every selectin a query is automatically launched in order to extract the available items for the following selection in the sequencial selection tree. b. The selection options (windows and buttons) in step 2 and 3 may be activated or inhibited according to the chosen display mode, for representation clearness or conceptual reasons. For example: the distinct category option for the data source selection is not selectable for the ADT per Origin/Destination display mode; the ADT survey month, day and day type windows are inhibited if the ADT per year or per Origin/Destination has been selected.

WP6 Web Application WB application now available on ALPCHECH Server for ALPCHECK community at: http://dh.alpcheck.eu/guiweb/index.htm

Thank You for attention