Database Support for Mobile Computing Applications

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Database Support for Mobile Computing
Applications

• R. Bayer
• Institut für Informatik
• TU München
• 3.6.2000
• Rudolf/Vorlesungen/DWH-SS2000/DWH-MobileComp

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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

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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?

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Car Traffic Management in Munich Assumptions 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
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Reads and Updates Updates to Drive-DB 1
update/(carmin) 105 cars1 update/(carmin)
105 updates/min 1.700 t/s Reads from
Route-DB travel time 30 minutes (105 cars/30
min)1 read/car 105 reads/(3060) sec
56 t/s
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Route Calculations Initial 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
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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?

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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
  745, go towards gate in 7 minutes
• On return parking ticket paid automatically,
  gate opens, when car approaches.

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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

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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
• HM has bikini special
• WOM Madonnas new single just arrived

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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 ...?

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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 ???

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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

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Emergency Service

• Pushing the panic button calls police or
  ambulance
• mobile phone transmits position and medical data
  of owner, voice of attacker

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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?
• ...

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• The Database Problem
• Types 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

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Attributes and Dimensions 1. 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) eating sho
pping arts 4 to 5 dimensions 4. State movie
playing now, number of free seats, ... probably
modeled like features of GfK
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• 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 !

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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 11105 variable subjects 1
update per minute 11105 /60 U/s 20.000
updates /s ? achievable with parallel DBMS !
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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!
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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