1. 1: IntroductionData Management & Engineering1 Course Overview: CS 395T Semantic Web, Ontologies and Cloud Databases Daniel P. Miranker Objectives: Get to know each other Set expectations

2. 1: IntroductionData Management & Engineering2 Course Requirements Several lab homeworks. (completion grade) –Build an ontology –Write SPARQL queries –Simple HADOOP exercise 2 paper presentations –(may overlap term project) Term project

3. 1: IntroductionData Management & Engineering3 Presentation Content Miranker will present about 1/2 of CS386d, Database Management Systems, in about 1/2 of the material’s normal time. Student presentations of papers.  Attendance is required

4. Papers Miranker will provide an initial set of papers Remainder of the class will be crowd-sourced. –Student’s each required to nominate >= 3 papers. –List is compiled. –Each paper is assigned to 3 referees (just like a conference). –Miranker organizes the class from the referee reports. 1: IntroductionData Management & Engineering4

5. Presentations Elements of Public Speaking. Structure –Two presentations, improvement will be noted –Draft slides due one week ahead of time, will be reviewed in a one-on-one meeting –Feedback from the class, Miranker not in the room 1: IntroductionData Management & Engineering5

6. 1: IntroductionData Management & Engineering6 Database Systems Getting Exciting Again ---> weren ’ t exciting for a long time.

7. 1: IntroductionData Management & Engineering7 In the recent past, DBMS witnessed: Commoditization –Database ---> database management system ----> relational database management system –Canonical RDBMS architecture is a mainstay. (and so are architectures for Operating Systems and Networks)

8. 1: IntroductionData Management & Engineering8 DBMS Architecture Query Engine Transaction Manager Storage Manager

9. 1: IntroductionData Management & Engineering9 DBMS Architecture Storage Manager Exploit memory hierarchy to compensate for slow disks. –working sets (from OS) –search algorithms Specifics –manage a heap of disk pages –allocation of main memory (buffer management) –index methods, e.g. B+ tree (access paths) Storage Manager RAM

10. 1: IntroductionData Management & Engineering10 Indexes data blocks RAM organized as page buffers

11. 1: IntroductionData Management & Engineering11 DBMS Architecture, 2 Transaction Manager Manage many users sharing a database, (speed) Cope with machine crashes –Every thing gets written at least 3 times. –Every DB write, also logged to redundant disks, (a.k.a. stable store) ACID properties –Atomic –Consistent –Isolated –Durable Transaction Manager Storage Manager log RAM

12. 1: IntroductionData Management & Engineering12 DBMS Architecture 3 Query Engine SQL execution environment –parse –compile to logical operators –optimize: Choose a good set of access paths and sequence of database operators (a.k.a. a physical plan) Query Engine Transaction Manager Storage Manager

13. 1: IntroductionData Management & Engineering13 DBMS Architecture 3 Query Engine SQL execution environment –parse –compile to logical operators –optimize: Choose a good set of access paths and sequence of database operators (a.k.a. a physical plan) Query Engine Transaction Manager Storage Manager

14. What Changed? 1: IntroductionData Management & Engineering14

15. 1: IntroductionData Management & Engineering15 What Changed? Internet Moore’s law –Computing is forever getting cheaper Processing Storage Bandwidth –People are not getting cheaper

16. Implications Business models founded on –  in the asymptote computing and bandwidth are _ _ e e Economy of scale People cost dominate  Data Centers  Computing as Services  Massive application of commodity components  (Electricity?) 1: IntroductionData Management & Engineering16

17. 1: IntroductionData Management & Engineering17 Software Engineering Implications A DBMS is a shared resource and the place to persist all data. Thus, unambiguously Content and programming of a DBMS is –at the center of –must interoperate with all the other software development

18. 1: IntroductionData Management & Engineering18 Three Tier Architecture –Pervasive? every hardware vendor sells a preloaded rack.

19. 1: IntroductionData Management & Engineering19 XML has become a standard for data transfer XML

20. 1: IntroductionData Management & Engineering20 Service Oriented Architecture XML Internet Service:= (usually) a remote database query or transaction

21. 1: IntroductionData Management & Engineering21 Three Tier Architecture –Pervasive? every hardware vendor sells a preloaded rack. –What does it mean if you know about databases? you ’ re a king –What if you are a professor of Computer Science?

22. 1: IntroductionData Management & Engineering22 Definitions (old slide 1) Database*: A collection of data Database Management System (DBMS): A software system that provides a set of services on a database. *A word on notation. Underlined terms are technical terms whose definition I expect you to know well.

23. 1: IntroductionData Management & Engineering23 Examples: (old slide 2) Relational Database Management System Operating Systems –What about operating systems? –__________ facebook

24. 1: IntroductionData Management & Engineering24 RDBMS Architecture Motived Core Database Architecture –How to cope with disks. The only [computational] moving part. Its not changing.

25. 1: IntroductionData Management & Engineering25 What about disks? Why do computers have disks? (good) –inexpensive, large persistent, storage. persistent storage: data is unaltered if the power goes off. Why do we wish they didn ’ t? (bad) –slow 8-12 msec. seek time. ~ 0.1 that in rotational latency –they break

26. 1: IntroductionData Management & Engineering26 Solid State Disk Drives! (SSD) Have been promised for 40 years Long term impact on DBMS architecture promises to be great. Current impact, negligible. Let’s look at the real numbers: ___________

27. 1: IntroductionData Management & Engineering27 Renaissance of Database Research Semantic Web Cloud Databases (NoSQL) Other Specialized Databases –General purpose database, all thing to all people. amortize cost of product over largest possible market –Today market is so large, functionality so broad focussed feature set --> more effective product market fragments are large enough to support specialized product.

28. 1: IntroductionData Management & Engineering28 Data Integration Query Engine Transaction Manager Storage Manager Semantic Web Knowledge-base techniques to simplify large-scale systems SPARQL/linked data query NoSQL Cloud Databases Non-ACID transaction models Fault-tolerance through redundancy vs. stable-store

29. 1: IntroductionData Management & Engineering29 Renaissance of Database Research Semantic Web –started with focus on search –now, data interchange and data integration Cloud Databases Other Specialized Databases –General purpose database, all thing to all people. amortize cost of product over largest possible market –Today market is so large, functionality so broad focussed feature set --> more effective product market fragments are large enough to support specialized product.