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This document is exclusively for students of EPFL following in 2007 the course on Ambient, Continuous«Ambient, Continuous & Mobile Data»& (given by Michel Adiba)Mobile Data It contains materials coming from different sources & people cited in the Michel Adibadocument. Grenoble University-FranceMichel.Adiba@imag.frPersonal use. No reproduction allowed© Michel Adiba-DACM-2007Content• DB & DBMS evolution : from DB to Data Space• Temporal, Spatial & Active DBGENERAL • DB Query & Optimization• Location Dependent QueriesINTRODUCTION • Continuous Queries • Data Streams and DSMS• Mobile Transactions• Conclusions© Michel Adiba-DACM-2007 © Michel Adiba-DACM-2007ηιDB & DBMS evolutionThe Nano-Yotta Paradigm• History-9•Nano10: devices are getting smallerPC, PDA, cell. Phone, smartcard, sensors, etc.•Ambient 24 • Yotta 10 : data are getting larger • Continuous (12) (15) (18) (21) (24)TB , PB , EB , ZB , YB+ Networking : WAN, LAN, WEB, Wireless, bluetooth,…• Mobile Data• From DB to Data Spaces • Ambient Intelligence, Ubiquitous Computing, MobilityData everywhere…How DB people deal with that ?© Michel Adiba-DACM-2007 © Michel Adiba-DACM-2007© Michel Adiba, Cours DACM 2007 1???????????9?9?9???9???9???????????Main Dimensions of Data ManagementHistory of DB & DBMSData : ≠types, ≠sizes, static (values), dynamic (operations)ActiveWEB –basedSpatial Models : ER, Relations, Objects, SSD, …Temporal DBMSMultimedia XML ...

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merise

Ambient, Continuous & Mobile Data

Michel Adiba Grenoble UniversityFrance Michel.Adiba@imag.fr

GENERAL

INTRODUCTION

DB & DBMS evolution

History Ambient Continuous Mobile Data From DB to Data Spaces

This document is exclusively for students of EPFL following in 2007 the course on

«Ambient, Continuous & Mobile Data» (given by Michel Adiba)

It contains materials coming from different sources & people cited in the document.

Personal use. No reproduction allowed

Content

DB & DBMS evolution : from DB to Data Space Temporal, Spatial & Active DB DB Query & Optimization Location Dependent Queries Continuous Queries Data Streams and DSMS Mobile Transactions Conclusions © Michel AdibaDACM2007

ηι The NanoYotta Paradigm

9  Nano 10 : devices are getting smaller PC, PDA, cell. Phone, smartcard, sensors, etc. 24 Yotta 10 : data are getting larger TB , PB , EB , ZB , YB (12) (15) (18) (21) (24) + Networking :WAN, LAN, WEB, Wireless, bluetooth,…

Ambient Intelligence, Ubiquitous Computing, Mobility Data everywhere…

How DB people deal with that ?

M.Adiba, DBTA, Berne, 03/03

Main Dimensions of Data Management

¾Data :≠types,≠sizes, static (values), dynamic (operations) ¾Models : ER, Relations, Objects, SSD, … ¾Persistency : Model, Storage ¾Query Languages & Interfaces : SQL,QBE, OQL, QL+PL… ¾Data Access & Query Optimization :I/O bound! ¾Data Integrity & Security : constraints, access rights, crypto,.. ¾Transaction Management : consistency, concurrency, recovery ¾Systems & Architectures :DBMS (from smartcard to DB machine), Client(s)/Server(s), Web, P2P/Grid, MOBILE…

Hierarchical & Network DBMS

1960

Relational DB In ONE slide! * ¾More than 30 Y (1970) : maturity! ¾Theoretical & Practical aspects (DBMS) st ¾Domains, R⊆D1 x D2 x .... Dn, Algebra,Predicate Logic1 Order ¾Languages : SQL (wins), QUEL, QBE (see MS Access)R x S R∪S ¾DBMS Prototypes (1975), Products (1980) R  S ¾A MAJOR improvement in DB : provide data independence R[α] & a simple, tabular VIEW of data. R :ϕ ¾Relations are implemented as Tables, Views & Snapshots  (NO “relation” in SQL) R * S

Integration Data Documents Services

Active Spatial Temporal Multimedia Object & OR Deductive

1980

1990

1970

Mobile Data Space Sensors DSMS

Data Warehouse Mining OLAP

ATOMS

CO Model

Data Models Evolution

History of DB & DBMS

tuple

w_name

r_capital

...

Data Semantics (J.R.Abrial) ER (P.Chen)

list

¾Large spectrum, // with Program. Languages ¾Hierarchical, Network ¾Systems)Relational, (+ ¾ER, Z, MERISE, UML… (+ Methods) ¾Semantic models ¾Data vs. Knowledge : Deductive DB ?? ¾Multidimensional models (OLAP)

DATA MODELS

M.Adiba, DBTA, Berne, 03/03

Objects & DB

r_name

tuple(name: "France", capital: v1, towns: set(v1, v2, v3) )

¾Mid80s : a new approach ? ¾Basic ideas : PL + DB, ObjectOriented Languages, Type extensibility, dynamic ¾Two “clans” : relational DB vs. OO DB ¾Now : integration and ObjectRelational DB ¾The best of the two worlds ;)

add_town

¾Complex Objects 9“relational was too flat” ¾Operations : ADT (UDT, UDF) 9“relational was too static” ¾SemiStructured Data Models 9Conciliate Documents and Data 9Deal with the Web & the XML Galaxy 9Schema less data

2000

WEB –based DBMS XML

2007

2005

* Not possible for any other proposed data model ! M.Adiba, DBTA, Berne, 03/03

Relational (T.Codd)

OLTP OLCP

Files

Complex Objects, Object Identity, Encapsulation, Types or Classes, Inheritance, Late Binding, Extensibility

Persistence, Storage managt, Security, Transactions, Liability, Concurrency Ad hocQuery.

MOV

Y > 1990

5! Possible Joins Access Paths Intra vs. Inter // ================= MOV & MD H.partitioned / 3 servers MOV=M1∪M2∪M3 M3 replicated M31, M32

DB Concepts

AC = S.Stone

RMT

Formalization

¾What vs. How : Data Independence ¾Non procedural, declarative languagesThe Web ¾SQL, QUEL, QBE SEQUEL (76), ANSI/ISOSQL (86) , SQL (89), SQL2 (92), SQL3 (99, 03),…? ¾SQL is not only a query language Structured ¾SQL is not a programming language Databases ¾Object Query Language OQL & OODB Pull vs. Push ? ¾XQueryThe WEB & Continuous Query http://www.w3.org/XML/Query Location dep. Query

¾Client(s) /Server (s)

¾Monolithic DBMS

¾Main Memory DBMS

Object Orientation

M.Adiba, DBTA, Berne, 03/03

The best of the two worlds?

PL & DB eg JDO ?

Distributed Query Optimization Towns where movies made after 1990 staring S.Stone ? MOV(T,C,Y,D) MD(T,AC) PR(T,TN,RN,W,NB) MT(TN,TOWN,PN) RMT(TN,RN,NB)

Active DBMS

ACID Transactions Transaction execution ¾Atomicity : all or nothing Case 2 ¾Consistency Case 1 ¾Isolation EifailureEf ¾Durability  OLTP & OLCP, TP Monitors  Benchmarks :www.tpc.org &Standards

User view: writing transactions is a software engineering problem System view: deal with Integrity, Concurrency & Recovery

Systems & Architectures partition and/or replication

time

 Generated vs. Handcrafted Triggers (New programming paradigm for application development or system tool)

Row / Statement

Triggers in DBMS & SQL3 (99)

Generalization : Active Rules

TN, RN

From Triggers to Active DB

TOWN,….

Appl. Prog. EVENT

Non active DBMS

Active Rule

DBMS

Event / Condition/ Action

Old Idea : Triggers (75)

Integrity Constraints

Before/ After / Instead

Old / New state

Query Languages

¾Very large spectrum of DBMS from smartcard to (parallell) DB Machine ¾HW Influences: machine architecture (P, M, D), networks, data storage units, shared disk, shared nothing ¾SW Influences: O.S. & Middleware

Ambiant, Continuous

DBMS Evolution

¾Need for ubiquitous Data Access

¾Challenges for Data Management Techniques : searching, location dependency (GPS), reliability, consistency

GRIDS for Eand m business, science, engineering eand m work, business and society Learning,egovernment

anywhere, anytime, anywhere, any service, for all’ core technologies & “pullthrough” applications

Example : European 6° Framework Program 20032006Î

Data

¾The ability to access any information from anywhere, at any moment, using any kind of devices

Knowledge & interface technologies

Knowledge technologies

Low bandwidth, separate networks …..…

Mobile telephony (voice) ……………….

“Word” based information search ….…

Towards an “all inclusive knowledge society”

“Ambient Intelli ence” tomorrow “Our surrounding” is the interface

Health, eInclusion, mobility, environment safety, cultural heritage

Communication, computing& software technologies

components & µsytems

IST for societal challenges

Mobile/Wireless full multimedia

sources

Information System

Infinite bandwidth, convergence, ..

IST for work & business challenges

Demandingapplications

Context based Semantic based Agent based Scaleable

Ex. see Nods Project :http://wwwlsr.imag.fr/Les.Groupes/STORM © Michel AdibaDACM2007

& Mobiles

DBMS

¾Distribution, Autonomy, Mobility : users, servers, data

Pervasive & Ubiquitous Computing Ambient Intelligence ¾Existence of Wired & Wireless Networks (eg WiFi)

Use all senses, intuitive

mobile

workstation

Software

Embedded Distributed Reliability Adaptability

Mobile: beyond 3G Fixed:All optical Integrated (IPv6) Audiovisual systems

Communicatio & networking

Security, privacy IPRs, dependabilty Smart cards,...

Trust & Security

Active

Distribution & Mobility

Contextbased knowledge handling

“Writing and reading” ….…………….…

Applied IST for major societal and economic challenges

interfaces

All senses Multilingual Intuitive ‘Surrounding’

IST toda PC based ………………………………

¾No more monolithic DBMS ¾Extensible, lightweight DBMS ¾Unbundled technology* ¾Componentbased architectures* (thickgrain vs. fine grain) ¾Components are providingServices ¾Blur the boundaries between OS & DBMS ¾Selfadaptive Systems ¾Multitier architectures, Web, P2P, GRID, … * See Dittrich, Geppert, Eds, “Component Database Systems”, MK 2000 * Chaudhuri & Weikum, Rethinking Database System Architecture: Towards a Selftuning RISCstyle Database System, VLDB 2000 © Michel AdibaDACM2007

Micro scale ……………………………

Silicon based ….…………………………

Information System Evolution

Wide adoption (eHealth,eLearning, …)

Nanoscale

+ new materials

eServices just emerging ………………

>70% of worldwide population on line

µ and nano systems

Multidiscplines New Sensing Networked New materials Nanoscale

CMOS : the limit SystemonChip Nanoscale New materials

, nano & opt electronics

Only5%ofglobalpopulationonline….

t1 t2 t3 t4 t5 History = {(ti,vi)} Set of tuple : (timestamp, value) Three important notions : - ti Periodicity - vi Modification - vi Persistency

Wireless Grafitti – Data, data everywhere, T.Imielinski, B.Nath, VLDB02 © Michel AdibaDACM2007

Data History

Several versions of an object during its lifetime

time

¾DB : locally stored data about remote physical objects ¾Dataspace  Data is inherently dispersed & connected  Data lives on the physical object  Data is stored with the physical object  Data is another characteristic of the object (like weight, color) ¾Objects in dataspace produce and store their own data

Virtual organisation

Data Integration Systems

Web Search

¾Mobility & location Mgt for millions of mobile terminals ¾Wireless networks & small devices ¾Sensors : road, environmental, pollution, vending machines, home sensor, light detectors, body sensors ¾Sensors produce and/or store data ¾Sensors can also be mobile

Dealing with TIME & Calendard Time : Discrete vs Continuous Time Line : Left bound (Big Bang), Right bound (Big Crunch) ? Querying historical data SQL extensions: SQL2, TempSQL, TQUEL, TSQL2 A lot of work in the past

Enterprise Portals

Scientific Repositories

Low

Semantic Integration

Far

Administrative Proximity

From DB to Dataspaces: a new abstraction for Information Management, M.Franklin, A.Halevy,D.Maier,ACM SIGMOD RECORD 2005 © Michel AdibaDACM2007

DBMS

High

DB vs. DataSpace (1)

DB vs. DataSpace (3)

Desktop Search

SpatialTemporal Data & Queries

DB vs. DataSpace (2)

Temporal & Historical BD

Near

Historical Data (2)

 History = {(ti,vi)}  Interpretation ? At time ti, the object value is vi  Separate history : construction (write) & exploitation (read)  « appendonly » DB semantic: adding tuples (ti, vi) without changing existing ones (what about correcting the past?)  Reading semantics is not obvious.

¾SQL extensionsSpatial Objects and ¾Spatial Operations, Relationships ¾Spatial Querying : designate an area on the screen ¾Graphical output ¾Spatial Criteria

SELECT FROM WHERE AND AND

road.geometry road, town town.name = "Orono" road.name = "Grove Street" road.geometry INSIDE town.geometry;

Location Dependent Queries (GPS) 1. Find the cheapest hotel in Paris 2. Find the cheapest and nearest hotel 3. Find the cheapest and nearest hotel / where I’ll be in one hour

1 : DB classical query 2 : necessary to locate the user (mobile or not mobile) and different results when time goes on… 3 : mobile user : where he/she is? What is the trajectory? Querying the future…

Cf. Moving Objects Databases, R.H.Guting, M.Schneider, MK, 2005 © Michel AdibaDACM2007

History : example

History = {(ti,vi)}

Remarks :  Ti granularity ?  Interpretation : in [ti, ti+1[ value is vi (between 30 & 39, value is 65)  Same values for different timestamps : meaning what?  Here we note a regular increase of ti  But time goes on….. Data streams ?

T 10 20 30 40 50 60 70 80 90

V 45 46 65 30 30 66 123 78 12

Topological & Directional Relationships

disjoint(A,B)

touch(A,B)

covered_by(B,A) covers(A,B)

inside(B,A) contains(A,B)

overlap(A,B)

A B equal(A,B)

LDQ : Localisation of Mobile Objects Mobile Units : vehicles, mobile phone, PC, PDA, … Mobile Users: localisation? Independently of the unit he/she used Mobile Code : agent Localisation, two extremes: – Look everywhere – Store the location every where Several solutions in this spectrum

Chrysanthis & Pitoura, “Mobile and Wireless Database Access for Pervasive Computing”, Tutorial ICDE2000© Michel AdibaDACM2007

Data

Continuous Queries & Data Streams

Query & Data Duality

Index

Data

From Michael Franklin, UC Berkeley, NRC June 2002

Result

Index

Queries

Another look at DB Queries

¾Traditional : Persistent Data and Transient Queries

¾Repetitive execution of the same query : Continuous Queries

¾Streams of Data : Transient Data & Persistent Queries

FOREVER DO Execute query Q Return results Sleep for some time ENLOOP

9Semantic of CQ? 9AppendOnly DB and Monotone queries

In The Beginning

Query

Index

Data

From Michael Franklin, UC Berkeley, NRC June 2002

Result

Query & Data Duality

Index

Data

From Michael Franklin, UC Berkeley, NRC June 2002

Result

Queries

“Continuous Queries  CQ”

Execution of a Query « forever »

Similar to Triggers & Active DB

Query definition, registration, activation, evaluation.

Query evaluation produces a continuous result

At the origin, CQ for “Appendonly” DB

CQ Execution FOREVER DO Execute query Q Return results to user Sleep for some period of time ENLOOP

¾Attention : result depends when Q is executed (eg, every hour)

¾At each execution, new results are produced and added to the previous one.

¾Inefficient execution if Q is re executed completely each time.

Continuous & Monotone

τ ∞ Set :=  FOREVER DO Set t := current time τ Execute queries QM(t) and QM( ) τ Return QM(t) QM( ) to user τ Set := t Sleep for some period of time ENLOOP

 Keep track of the last result of Q  This algorithm works when Q is monotone, i.e. M Q (t )⊆when t <tQ (t ) M 1 M 2 1 2

To or not to be Monotone?

monotone : Q(t1)⊆Q(t2 ) when t1<t2

Beware of time reference inside the Query

Example Getdate() in SQL for the current date

E < Getdate() or E≤Getdate()YES

E > (or =,≥,≠) Getdate()

Select * From m where m.expire > getdate()

Pbs also with NOT EXISTS

“CQ Semantics”

Problem of non determinism because tuples selected by Q depend on when Q is executed (every hour)

Semantics of « continuous » ?

The results of a continuous query is the set of data that would be returned if the query were executed at every instant in time.

How to implement this in a DBMS ?

Monotone Queries

monotone : Q(t1)⊆Q(t2 ) when t1<t2

 Some are naturally monotone, others can be converted and some are not  Attention « appendonly » DB Select * From T where T.A= 3  Selections, joins, are in general Monotone

Monotone Query (2)

Select * From T where T.BαGetdate() Ifα< : monotone True

False T.B Select * From T where T.BαGetdate() IfαNON monotone> : False

True

T.B

Msg M1 M2 M3 M4 M5 M6 M7 M8

Example Q(t1)⊆Q(t2 ) when t1<t2

D 10 10 20 30 30 40 40 50

D < Date : Result ? Date = 5 { } Date =12 {M1, M2} Date = 25 {M1, M2, M3} Date = 45 {M1, M2, M3, M4, M5, M6, M7}

D > Date : Result ? Date = 5 {M1, M2, M3, M4, M5, M6, M7, M8} Date =12 {M3, M4, M5, M6, M7, M8} Date = 25 {M4, M5, M6, M7, M8} Date = 45 {M8}

Data Streams Applications

Applications generating data streams –Network monitoring and traffic management –Call detail records in telecommunications –Transactions in retail chains, ATM operations in banks –Log records generated by Web Servers –Sensor network data,RFID tags –Financial applications –Manufacturing processes Characteristics of these applications – Large volume of data (+ terabytes) – Records coming with a high rate (with or without timestamp) – Data may come from mobile or ambient units

Problems : patterns findings, queries, real time statistical analysis on data streams

Sensor Networks Samuel Madden, UC Berkeley, December 2002, http://www.cs.berkeley.edu/~madden/berkeley.htm

Habitat Monitoring: Storm petrels on great duck island, microclimates on James Reserve.

Earthquake monitoring in shake-test sites.

Vehicle detection: sensors along a road, collect data about passing vehicles.

CQ & Data Streams

Unlimited sequence of data or events Very often associated to a temporal attribute –Timestamp (generated + or  automatically) –Logical time (application dependent) Continuously generated events –“pushed” or “pulled” from producers to consumers CQ execution on one or several data streams

Time from data source t 16 p 3

t 15

t 14 p 2

t 13

t 12 p 1

t 11

t 10

stream p

IP Network Measurement Data

IP session data (collected using NetFlow) Source Destination Duration Bytes Protocol 10.1.0.2 16.2.3.7 12 20K http 18.6.7.1 12.4.0.3 16 24K http 13.9.4.3 11.6.8.2 15 20K http 15.2.2.9 17.1.2.1 19 40K http 12.4.3.8 14.8.7.4 26 58K http 10.5.1.3 13.0.0.1 27 100K ftp 11.1.0.6 10.3.4.5 32 300K ftp 19.7.1.2 16.5.5.8 18 80K ftp

AT&T collects 100 GBs of NetFlow data each day!

300 M call tuples /day (long distance) 10 B IP flows /day (backbone)

Input Stream

CQ on Data Streams

Result : 9Stream?

9Relation? 9Storage?

CQ Result is potentially unlimited Storage : part of input, part of output? Operators : non blocking (e.g. selection) or blocking (e.g. join)

Data Stream Processing*

A typical streaming query

Window SELECT S.city, AVG(temp) Clause FROM SOME_STREAM S [range by ‘5 seconds’ slide by ‘5 seconds’] WHERE S.state = ‘California’ GROUP BY S.city Window “I want to look at 5 “I want a result tuple seconds worth of data” every 5 seconds” Data Stream … Result Tuple(s)Result Tuple(s)

* From HiFi project, Berkeley

Sam



lin

: Basics

Idea: A small random sample S of the data often well-represents all the data – For a fast approx answer, apply “modified” query to S – Example: select agg from R where R.e is odd (n=12) Data stream: 9 3 5 2 7 1 6 5 8 4 9 1

Sample S: 9 5 1 8

answer: 5 – If agg is avg, return average of odd elements in S – If agg is count, return average over all elements e in S of  n if e is odd answer: 12*3/4 =9  0 if e is even

Unbiased:For expressions involving count, sum, avg: the estimator is unbiased, i.e., the expected value of the answer is the actual answer Garofalakis, Gehrke,Rastogi,SIGMOD’02 #57

DSMS : problems

Relations : set of tuples or sequences? DB updates? Append only ? Continuous and Snapshot Queries ? Exact or approximate result ? One pass query evaluation ? Access plan fixed or adaptive ? Limited resources (ex. memory) Real time processing of data streams

L.Golab, T.Ozsu, Issues in Data Stream Management, Sigmod Record, June 2003

Problems for stream processing

¾Taking into account arrival rate of data

¾Sometimes it is impossible to get a precise result. Approximation ? Sample ?

¾Joins : problem to join two unlimited streams. Window(s) ? Approximation ?

¾Computations : sum, count, avg, group by, etc. different kinds of functions

DBMS versus DSMS DSMS= Data Stream Management System

Persistent Relations Snapshot Queries Random access Access Plan is determined by query processor and by the physical organization of the DB

Dynamic Streams (& persistent relations)

Continuous Queries

Sequential Access Data characteristics and formats of arriving data non predictable

The 8 Requirements of Real-Time Stream Processing Michael Stonebraker, Ugur Çetintemel, Stan Zdonik SIGMOD RECORD Vol34 NO4, Dec 2005

Rule 1: Keep the Data Moving Process messages “instream”, without any requirement to store them to perform any operation or sequence of operations. Ideally the system should also use an active processing model. Rule 2: Query using SQL on Streams (StreamSQL) Support a highlevel language with builtin extensible stream oriented primitives and operators (e.g. Window). Rule 3: Handle Stream Imperfections (Delayed, Missing and OutofOrder Data) Rule 4: Generate Predictable Outcomes A stream processing engine must guarantee predictable and repeatable outcomes. Rule 5: Integrate Stored and Streaming Data Have the capability to efficiently store, access, and modify state information, and combine it with live streaming data. For seamless integration, the system should use a uniform language when dealing with either type of data. Rule 6: Guarantee Data Safety and Availability Ensure that the applications are up and available, and the integrity of the data maintained at all times, despite failures. Rule 7: Partition and Scale Applications Automatically A stream processing system must be able to distribute its processing across multiple processors and machines to achieve incremental scalability. Ideally, the distribution should be automatic and transparent. Rule 8: Process and Respond Instantaneously A stream processing system must have a highlyoptimized, minimaloverhead execution engine to deliver realtime response for highvolume applications © Michel AdibaDACM2007

DSMS Projects

– STREAM(Stanford) – Aurora& Borealis (Brown, Brandeis, MIT) – TelegraphCQ & TinyDB(Berkeley) Cooperation for a “stream system benchmark” Others : Hancock, Gigascope (AT&T) , NILE (Purdue), StreamMill (UCLA), etc. A large number of papers, tutorials, some books, A very active area Impossible to have a detailed view: choice

Handoff

DB U

Mobile Environments

CellWireless LAN (11 Mbps)

DB U MU

Fixed Network FH DB FH

DB MU MU DB MU

FH Fixed Host

BS Base Station

MU Mobile Unit

CellWireless radio (9 Kbps  2 Mbps)

Database

Mobile Network (MN) Fixed Network (FN)

Mobile DB & Transactions Transactions :new models (ACID?) – Mobile Transaction : a transaction where at least one MU is involved in the execution Products: PointBase, Navajo de Poet, Oracle Lite, DB2 Every Place, Sybase iAnywhere, SQL Server CE Research: Clustering, Twotier replication, Promotion, Reporting, Semanticsbased, Kangaroo transactions, MDSTP, Moflex transactions… P.SERRANO, C.RONCANCIO, M.ADIBA, “A survey of Mobile Transactions” International Journal on Distributed &Parallel Databases 16 (2): 193230, September 2004 ,

Recovery – Network partitioning is frequent – Disconnection is not (always) a failure – More logging

Mobile Transactions

Mobile environment characteristics

¾Frequent disconnections ¾Variable bandwidth (55kbs or 100Mbs) ¾Communication cost maybe high ¾MU have “limited” capabilities Batteries Computing power Secondary storage

Course Content

¾Moving Objects DB & Location Dependent Queries ¾Stream Data Management: general introduction & OLAP/Data Mining reminder Sensor DB: tinyDBMS & Tiny SQL STREAM Project (Stanford) AURORA, BOREALIS & StreamBase (Product) Stream Management : other issues ¾Mobile Transactions

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