3.6.2000R. Bayer, DB for Mobile Comp.1 Database Support for Mobile Computing Applications R. Bayer...

3.6.2000 R. Bayer, DB for Mobile Comp. 1

Database Support for Mobile Computing Applications

R. Bayer

Institut für Informatik

TU München

3.6.2000Rudolf/Vorlesungen/DWH-SS2000/DWH-MobileComp


Future Mobile Phones

• contain WEB browser

• have UTMS bandwidth 1.2 Mb/s

• integrated with GPS, i.e. exact position of phone and owner is known

entirely novel applications


Car Traveler on Road

• Car knows destination, gas consumption, gas remaining

• Questions and services:– reachable cheapest gas station along route?– next BMW repair station?– restaurant with in 100 km serving asparagus or

lobster?


Car Traffic Management in MunichAssumptions:

106 cars registered

105 cars traveling

Route-DB for preplanned routes, 10 per car, 104 B/route

106 cars * 10 routes/car * 104 B/route = 1011 B = 100 GB

Drive-DB

105 cars *1 route/car * 104 B/route = 109 B = 1 GB


Reads and UpdatesUpdates to Drive-DB: 1 update/(car*min)

105 cars*1 update/(car*min) =

105 updates/min ~ 1.700 t/s

Reads from Route-DB: travel time = 30 minutes

(105 cars/30 min)*1 read/car =

105 reads/(30*60) sec ~ 56 t/s


Route CalculationsInitial Routes: compute one optimal route on departure of car based on present traffic situation in Drive-DB

56 routes/s

Adaption of Routes: with every update of own position, car wants to know optimal route, in most cases the same as before?

1.700 checks/s


City Tourist• What is this? ( I am standing in front of?)

I want to know more, what is the URL?

• Where is the next bus or subway station, taxi point, public toilet, please guide me to it (load down part of city map with direct route inserted or text: )

• Where is medium priced fish restaurant within 500 m? Show it, make a reservation.

• I need an antihistamine, where is the next open pharmacy?


Air Traveler

• T is approaching airport Munich by car, server knows flight F and car or subway.

• „Proceed to parking P1, level 2, space 437“• „Mr. Bayer, you are checked into flight LH451 to

Cologne, leaving from gate A17, boarding time is 7:45, go towards gate in 7 minutes

• On return: parking ticket paid automatically, gate opens, when car approaches.


Truck Management

• What is position of trucks near Cologne with at least 1 ton loading capacity and slack of 1 hour in their delivery schedule?

• Show me planned travel route and stops of truck 327


Shoppers: Info pull or push?

• On entering Kaufhof, select WEB server

• Information pull:– guide me to the perfume dept.– bestsellers in detective stories?

• Information push (to close shoppers):– McDonald coming up 120 meters to right– H&M has bikini special– WOM: Madonna‘s new single just arrived


Nightlife

• Which friends are in Schwabing now?

• Who is DJ at P1?

• Live video shot with soundtrack from P1. ODODO, Kunstpark Ost

• „My position is ...“

• „Anybody close to share taxi to ...?


Sales Representative

• On approaching customer C: download the relevant marketing materials, sales and delivery data for C, combined with DWH.

• Show homepage and picture of person I meet• Did C have any reclamations recently?• Any significant changes in buying pattern of C

recently?• Question: B2B applications ???


Taxi Service

• Please pick me up to go to Kentvale apartments

• Taxi server finds optimal taxi: „Mr. Bayer, please stay where you are, a blue Comfort Taxi with plate SHA-488 368C will pick you up in approximately 3 minutes


Emergency Service

• Pushing the panic button calls police or ambulance

• mobile phone transmits position and medical data of owner, voice of attacker


Hotel Guest

• Theatre in walking distance showing movie with Meryl Streep starting around 20 h? Show map with route.

• reserve and pay ticket

• nearby bar serving Heineken?

• ...


The Database ProblemTypes of Subjects:

• Fixed location

• constant state: buildings, statues, paintings in museums, with a lot of additional information

• variable state: restaurants with seats

• Mobile• constant state, if subject refuses to disclose state • variable state


Attributes and Dimensions1. Location: 2 or 3 dimensions

2. Time: for tractable subjects like trucks

3. Classification of Subjects: hierarchies and MHC ~ subject ID, e.g.

automotive (cars, gas stations, parking, repair stations)eatingshopping arts 4 to 5 dimensions

4. State: movie playing now, number of free seats, ...probably modeled like features of GfK


Database Size (Mengengerüst)

1. Assumptions about Size

• 107 mobile subjects in Bavaria * 1000 B = 10 GB

• 105 fixed subjects, which are constant, but with 105

Bytes for image = 1010 B = 10 GB

• 105 variable subjects * 10 KB = 1 GB

11 GB with high update frequency !


2. Assumptions about Updates and Queries

A person is moving at most 10% of the time, i.e. 2.5 hours per day?

Update rate 1 per minute for variable subjects

11*105 variable subjects * 1 update per minute

= 11*105 /60 U/s ~ 20.000 updates /s

achievable with parallel DBMS !


Crosscheck for Airport Munich

20 flights /h* 300 passengers/flight =

6.000 passengers / h ~ DB size 60 MB

10 updates or queries per passenger?

60.000 t/h = 1000 t/m ~ 20 t/s

1 Server handles problem, second server for redundancy and mirroring DB!


Hurdles• Data Acquisition

– Mobile subjects: Telekom companies

– Fixed constant subjects: 105 * 10 DM = 1 million DM

– Variable subjects: free data via advertising

• Business Model– network providers

– content providers

– SW and technology providers

3.6.2000R. Bayer, DB for Mobile Comp.1 Database Support for Mobile Computing Applications R. Bayer...

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