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Modules Offered by the IDB Group Modules Offered by the IDB Group
Temporal Information
Systems(MA-INF 3302)
Temporal Temporal Information Information
SystemsSystems(MA(MA --INF 3302)INF 3302)
Seminar
Selected Topics inIntelligent IS
(MA-INF 3210)
SeminarSeminar
Selected Topics inSelected Topics inIntelligent ISIntelligent IS
(MA(MA --INF 3210)INF 3210)
Intelligent Information
Systems(MA-INF 3203)
Intelligent Intelligent Information Information
SystemsSystems(MA(MA --INF 3203)INF 3203)
Lab
Intelligent Information Systems
(MA-INF 3313)
LabLab
Intelligent Information Intelligent Information SystemsSystems
(MA(MA --INF 3313)INF 3313)
WS
SS
WS+SS
IDB (Intelligent Databases) GroupIDB (Intelligent Databases) Group::Prof. Dr. Rainer Manthey Prof. Dr. Rainer Manthey PD Dr. Andreas BehrendPD Dr. Andreas BehrendSahar Vahdati, MScSahar Vahdati, MSc**
(* partially with Prof. Auer(* partially with Prof. Auer‘‘ s group)s group)
Prof. Dr. Rainer Manthey:Prof. Dr. Rainer Manthey:
PD Dr. Andreas Behrend:PD Dr. Andreas Behrend:
•• 28.2.201928.2.2019: : Day of Day of RRetirementetirement•• => 5 semesters of teaching left (only in MSc program):=> 5 semesters of teaching left (only in MSc program):
•• PD/Habilitation: Full qualifications for any kind ofPD/Habilitation: Full qualifications for any kind ofacademic teaching (incl. thesis supervision), indepenacademic teaching (incl. thesis supervision), indepen--dent teaching scheduledent teaching schedule
•• Position at Uni Bonn Position at Uni Bonn endsendsat at 30.6.201830.6.2018(at latest)(at latest)•• At most 3 semesters of teaching left:At most 3 semesters of teaching left:WSWS 16/17, 16/17, SSSS17, 17, WSWS 17/1817/18•• Last two years: Guest professor at universities in Dresden, MLast two years: Guest professor at universities in Dresden, Marburg, Hallearburg, Halle•• TThis semester: teaching in Bachelor and MSc LSIhis semester: teaching in Bachelor and MSc LSI•• Next semester(s): teaching still openNext semester(s): teaching still open
Department I: Department II: Department I: Department II: Department III:Department III:
Prof. Ro. KleinProf. Ro. Klein Prof. Re. Klein Prof. WrobelProf. Re. Klein Prof. WrobelProf. KratschProf. Kratsch Prof. Weber Prof. Weber Prof. MantheyProf. Manthey
Prof. Hullin Prof. GallProf. Hullin Prof. GallJun.Jun.--Prof. Schultz Prof. Schultz Prof. AuerProf. AuerJun.Jun.--Prof. YaoProf. Yao Prof. LehmannProf. Lehmann
Department IV: Department V: Department VI:Department IV: Department V: Department VI:
Prof. Martini Prof. RProf. Martini Prof. Rööglin glin Prof. BehnkeProf. BehnkeProf. MeierProf. Meier Prof. BlumProf. Blum Prof. AnlaufProf. AnlaufProf. SmithProf. Smith Prof. BennewitzProf. Bennewitzapl. Prof. Kurthapl. Prof. KurthJun.Jun.--Prof. ReinhardtProf. Reinhardt
((Department IIIDepartment III: in R: in Röömerstramerstraßße; all e; all other departmentsother departments: in Friedrich: in Friedrich--EbertEbert--Allee)Allee)
““ GeographyGeography”” of Our Instituteof Our Institute
Groups in Department III
•• Main area of Research (and Teaching) in Department III: Main area of Research (and Teaching) in Department III: Intelligent SystemsIntelligent Systems
•• Until 2013: Information Systems and Software EngineeringUntil 2013: Information Systems and Software Engineering
•• Since then: Since then: New department headNew department headProf. Wrobel Prof. Wrobel •• Who is also Director of the Fraunhofer Institute IAIS and DireWho is also Director of the Fraunhofer Institute IAIS and Director of Bctor of B--ITIT
((IAISIAIS: Intelligent Analysis and Information Systems;: Intelligent Analysis and Information Systems;BB--ITIT: Bonn: Bonn--Aachen International Center for Information Technology)Aachen International Center for Information Technology)
•• Recently: Two big Recently: Two big new research groupsnew research groupsfounded (associated with IAIS as well)founded (associated with IAIS as well)•• EIS EIS –– Enterprise Information SystemsEnterprise Information Systems(Prof. Auer) (Prof. Auer) ~ 40 members~ 40 members•• SDA SDA –– Smart Data AnalyticsSmart Data Analytics(Prof. Lehmann) (Prof. Lehmann) ~ 20 members~ 20 members
•• Since 2013: Since 2013: Computer VisionComputer Visiongroup (Prof. Gall) group (Prof. Gall) 10 members10 members•• Since 1992: Since 1992: Intelligent DBIntelligent DBgroup (Prof. Manthey) group (Prof. Manthey) 2 members2 members
•• Teaching (main lectures in MSc): Teaching (main lectures in MSc): WrobelWrobel: Machine Learning, Data Science & Big Data: Machine Learning, Data Science & Big DataAuerAuer: Semantic DataWeb Technologies, Enterprise Information Systems: Semantic DataWeb Technologies, Enterprise Information SystemsLehmannLehmann:: Knowledge Graph AnalysisKnowledge Graph AnalysisGallGall: Computer Vision I + II: Computer Vision I + IIMantheyManthey: Intelligent IS, Temporal IS: Intelligent IS, Temporal IS
•• Lecture and exercises in the Lecture and exercises in the samesameroomroom (A 207).(A 207).
•• On On WednesdaysWednesdaysspecial special timingtiming conventions:conventions:•• Lecture starts 15 minutes Lecture starts 15 minutes later later than usual: than usual:
10:3010:30rather than 10:15 a.m..rather than 10:15 a.m..•• 45 minutes break 45 minutes break between lecture and exercises between lecture and exercises
(rather than just 30 minutes)(rather than just 30 minutes)•• Exercises start 30 minutes Exercises start 30 minutes later later than usual: than usual:
12:12:4545 rather than 12:15.rather than 12:15.•• Exercises end 30 minutes Exercises end 30 minutes later later than usual: than usual:
14:1514:15rather than 13:45.rather than 13:45.•• NextNext lecture (Prof. Auer) starts 14:lecture (Prof. Auer) starts 14:3030 rather than 14:15!rather than 14:15!
Exercises and Exams: Exercises and Exams: „„ Rules of the GameRules of the Game““
•• ExercisesExercises::•• In the In the samesameroom every Wednesday, following the lecture after 45 minutes brroom every Wednesday, following the lecture after 45 minutes break,eak,
for the entire auditorium, for the entire auditorium, nono small groups. small groups. •• Exercises held by Prof. Manthey and/or Exercises held by Prof. Manthey and/or Sahar Vahdati.Sahar Vahdati.•• Goals: Goals:
•• To make you fit for the exam!To make you fit for the exam!•• Attention!Attention! There are There are too fewtoo fewexercise weeks (for a long lecture)!exercise weeks (for a long lecture)!•• To provide some To provide some „„ hands onhands on““ experience with theoretically introduced concepts.experience with theoretically introduced concepts.
•• ParticipationParticipationwill will notnot be checked, but is strongly be checked, but is strongly recommendedrecommended!!!!•• NoNo prerequisites for getting prerequisites for getting admissionadmissionto exams! to exams! •• NoNo „„ homeworkhomework““ to be delivered, but motivation/encouragement for individualto be delivered, but motivation/encouragement for individual
activity provided in exercises. activity provided in exercises. •• NoNo individual feedback possible.individual feedback possible.
•• ExamsExams::•• RegistrationRegistrationfor exams: December 1 till 22 for exams: December 1 till 22
(online via BASIS)(online via BASIS)•• Written examsWritten examsfor both exam dates for both exam dates
(120 minutes duration)(120 minutes duration)•• Exam Exam dates to be determineddates to be determined: :
Most likely end of February + end of MarchMost likely end of February + end of March
There is There is no textbookno textbookwhich could be recommended for this lecture . . .which could be recommended for this lecture . . .
. . . . . . just the slidesjust the slidesserve as a substitute insteadserve as a substitute instead(representing a compromise between a good(representing a compromise between a goodbackground presentation and too much text)background presentation and too much text)
Only a small fraction of the attendees will have achance to get a place inseminars or to do a masterthesis in this area!
But:But:
Only a Only a small fractionsmall fractionof of the attendees will have athe attendees will have achance to get a place inchance to get a place inseminars or to do a masterseminars or to do a masterthesis thesis in this areain this area!!
IIS 2015: Over 100 participants in the exam !!IIS 2015: Over IIS 2015: Over 100 100 participants in the exam !!participants in the exam !!
This is the most commonly agreed view on the concept of an IS in informatics –provided people agree on the meaning of DBS!!
This is the most commonly agreed view on the concept of an IS inThis is the most commonly agreed view on the concept of an IS ininformatics informatics ––provided people agree on the meaning of DBS!!provided people agree on the meaning of DBS!!
This lecture will be more accurately concerned withThis lecture will be more accurately concerned with
rather than withrather than with
The naming of the module is more a matter of convention rather tThe naming of the module is more a matter of convention rather than precision!han precision!
Query Languages vs. Programming LanguagesQuery Languages vs. Programming Languages
DBDBDBMSDBMS
Data DictionaryData DictionaryData Dictionary
•• „„ RealReal““ DBMS support a separate kind of DBDBMS support a separate kind of DB--specific specific „„ programming languageprogramming language““ forforaccessing and manipulating data in the DB: accessing and manipulating data in the DB: query languagequery language
•• In contrast to the external imperative programming languages,In contrast to the external imperative programming languages,a query language isa query language isusually a usually a declarativedeclarativelanguage, the performance of which is optimised by the DBMS.language, the performance of which is optimised by the DBMS.
•• „„ ProgramsPrograms““ of the query language may be stored in the of the query language may be stored in the data dictionarydata dictionarywithin the DB. within the DB.
Relational Data Model and SQLRelational Data Model and SQL
•• The most widely used data model nowadaysThe most widely used data model nowadaysis the is the relational modelrelational model(introduced around 1970).(introduced around 1970).Relations are the mathematical basis for dataRelations are the mathematical basis for datarepresented in tables (rows/columns).represented in tables (rows/columns).
•• All relational DBMS support a predominantAll relational DBMS support a predominantdeclarative query language based on declarative query language based on logicallogical andandalgebraicalgebraicoperators:operators:
Background in Relational Databases and SQL: Strictly Necessary !Background in Relational Databases and SQL: Strictly Necessary !
A A good backgroundgood backgroundin relational databases andin relational databases andin SQL is expected from everybody attendingin SQL is expected from everybody attendingthis lecture!! SQL will frequently be used duringthis lecture!! SQL will frequently be used duringthe semester, even though we are going to learnthe semester, even though we are going to learna different relational language!a different relational language!
Material for selfMaterial for self--studystudy(in case your background is(in case your background isweak, dated, or missing):weak, dated, or missing):
•• Extra Extra slidesslidesvia IISvia IIShomepagehomepage
•• Cheap and easy Cheap and easy tutorialstutorialsfrom the Schaumfrom the Schaum‘‘ s seriess series
IDBS: IDBS: „„ Intelligent ServicesIntelligent Services““ in a DBMSin a DBMS
DBDBDBMSDBMS
DBSDBS
genericgenericspecificspecific
Certainly required: Certainly required: „„ IntelligentIntelligent““ behaviour of the DBS, behaviour of the DBS, i.e.,i.e., generic (applicationgeneric (application--independent) servicesindependent) servicesinside the DBMSinside the DBMS, able to , able to „„ simulate intelligencesimulate intelligence““
IDBS: IDBS: „„ KnowledgeKnowledge““ Inside a DBInside a DB
DBDBDBMSDBMS
DBSDBS
genericgeneric
specificspecific
Also certainly required: Also certainly required: „„ KnowledgeKnowledge““ about the resp. application domain about the resp. application domain in the DDin the DD(Data Dictionary)(Data Dictionary)
„„ KnowledgeKnowledge““ : : RulesRulesfrom the application domain as a basis forfrom the application domain as a basis fordrawing intelligent conclusions from stored datadrawing intelligent conclusions from stored data
Approach favoured by our research group (and thus in this lecturApproach favoured by our research group (and thus in this lecture):e):•• Try to reach as much Try to reach as much „„ intelligenceintelligence““ as possible using as possible using existing DB technologyexisting DB technology!!•• Identify weaknesses of this technology and think about reasoIdentify weaknesses of this technology and think about reasonablenable
extensionsextensions, without leaving the DB context! , without leaving the DB context!
At the core of IIS: Theory and Practice of Deductive DatabasesAt the core of IIS: Theory and Practice of Deductive Databases
This approach This approach –– which is a special one which is a special one ––explains the drawing on the title slide ofexplains the drawing on the title slide ofthis lecture.this lecture.
Therefore:Therefore:Theory and Practice of the establishedTheory and Practice of the establishedresearch area of research area of „„ Deductive DatabasesDeductive Databases““will be at the core of this lecture.will be at the core of this lecture.
The essence of this area of research can beThe essence of this area of research can bedescribed as follows:described as follows:
How to analyse data using stored queriesHow to analyse data using stored queries(in SQL: (in SQL: viewsviews) that serve as declarative) that serve as declarativeanalytical programs?analytical programs?
•• used in used in industryindustryand commerceand commerce•• supported by many DBMS supported by many DBMS productsproducts•• standardizedstandardized•• useruser--friendlyfriendly ((„„ controlled Englishcontrolled English““ ))•• rich rich set of syntactic featuresset of syntactic features
•• used in used in academiaacademiaonlyonly•• just few academic just few academic protoypesprotoypes•• nono standardsstandards•• mathematicalmathematicalstylestyle•• minimalisticminimalisticsyntaxsyntax
•• Research in deductive databases has a nearly 40Research in deductive databases has a nearly 40--years history (as old as SQL), butyears history (as old as SQL), buthas been using a has been using a differentdifferentdeclarative language (not SQL!) most of the time, stronglydeclarative language (not SQL!) most of the time, stronglyinfluenced by the logic programming language PROLOG:influenced by the logic programming language PROLOG:
•• Nearly all publications in this area have been using Nearly all publications in this area have been using DatalogDatalog–– thatthat‘‘ s why we will uses why we will useDatalog during this lecture, too (and you will have to learnDatalog during this lecture, too (and you will have to learnit!).it!).
•• Many results of DDB research have been transferred to the Many results of DDB research have been transferred to the SQLSQL world recently!world recently!ThatThat‘‘ s why SQL will also be appearing throughout the lecture in varios why SQL will also be appearing throughout the lecture in various places.us places.
1.1. Organisation and MotivationOrganisation and Motivation 1 lecture1 lecture
2.2. Deduction in Datalog and SQL Deduction in Datalog and SQL 7 lectures7 lectures3.3. Semantics of Deductive DatabasesSemantics of Deductive Databases 6 lectures6 lectures4.4. Efficient Query Evaluation in DDBsEfficient Query Evaluation in DDBs 6 lectures6 lectures5.5. Efficient Update Propagation in DDBsEfficient Update Propagation in DDBs 7 lectures7 lectures
6.6. PerspectivesPerspectives 1 lecture1 lecture
relevant forrelevant forexamexam
This is how the lecture will be structured This is how the lecture will be structured –– the number of lectures might be slightly varying the number of lectures might be slightly varying in in „„ real lifereal life““