Detec%ng the Temporal Context of Queries. Oliver Kennedy, Ying Yang, Jan Chomicki, Ronny Fehling, Zhen Hua Liu, and Dieter Gawlick 09/01/2014

Transcription

1 Detec%ng the Temporal Context of Queries Oliver Kennedy, Ying Yang, Jan Chomicki, Ronny Fehling, Zhen Hua Liu, and Dieter Gawlick 09/01/2014

2 Outline Mo.va.on Contextual Analysis Prac.cal Temporal Dependency Detec.on Experiment

3 Mo.va.on Contextual Dependency Errors in ETL Name Account type balance Alice saving 10,000 Bob checking Name Account type balance Alice saving 0 Bob checking 500 Since ETL processes mask the source data and the transforma.on, the change in different stage might be misleading for analysts.

4 Mo.va.on Op.mizing Opera.onal Business Intelligence For each item, there is a threshold for quan.ty. We want to add more goods when the quan.ty is below the threshold. e.g. Threshold of quan.ty for P1345 is set to be 400 and for Pt454 is set to be 30. PARTKEY SUPPKEY QUANTITY PRICE P1345 S Pt454 S ,000 q Such thresholds are determined manually and are typically sta%c.

5 Mo.va.on Op.mizing Opera.onal Business Intelligence q Predic%ng the op%mal thresholds based on live sales data has resulted in higher efficiency and profits. q However, predic.ng these thresholds can be very challenging and costly.

6 Mo.va.on Vola.lity Analysis If a column historically has a certain update rate, and suddenly that rate changes outside of the norm, how do we figure out what the norm is? Can we monitor the difference efficiently?

7 Mo.va.on A lot of assump.ons are made on data. (e.g. assump.ons made during extrac.on, transforma.on, and schema and query crea.on etc.) They are so implicit that (1) it is not easy to detect a mistaken one and (2) hard to explain the result. (why the result changes on the same data and query? Or why certain tuple not in the result set?) For example, when we aggregate temperature on a table, we may assume it is the temperature for a period of %me. When doing ETL, we may assume for some column, the value null means 0, but later it may mean others.

8 Mo.va.on What do people do right now? List all the assump%ons. Why are these solu.ons not good enough? To many assump%ons and the database is too large. Why would not all of these problems be solved through good schema design? Good schema can not compress the tuples.

9 Contextual Analysis What are the assump.ons? We are going to call the set of assump.ons context. What is context? Contexts for the assessment and usage of a data source at hand are modeled as collec%ons of external databases, that can be materialized or virtual, and mappings within the collec%ons and with the data source at hand 1. 1.From Data quality is context dependent by Leopoldo Bertossi,Flavio Rizzolo and Lei Jiang.

10 Contextual Analysis What is context? Context is the metadata for Name Account type balance Alice saving 10,000 Bob checking 500 Name Account type balance Alice saving 0 Bob checking 500 the balance is AVERAGE over %me.

11 Contextual Analysis What is context? G ID Date Time Temperature(Context) /08/30 12:00 C:28 F: /08/30 24:00 C:10 F:50 D C ID Date Time Temperature(Context:C) /08/30 12: /08/30 24:00 10 D F ID Date Time Temperature(Context:F) /08/30 12: /08/30 24:00 50

12 Contextual Analysis Time as Context An analyst decides to run a new promo%on for customers with low balances. T 1 - T n Name Account type balance Alice saving 0 T n+1 Bob checking 500 Name Account type balance Alice saving 1000 Bob checking 500 Q: SELECT * FROM D WHERE balance <1000 Name Account type balance Alice s balance remains to be less than 1000 for long, she should be in the resultset. Bob checking 500

13 Contextual Analysis Detec.ng temporal dependencies We have N versions, 1 query. We want to evaluate the query on each of them and summarize the results in a well- understood way. a. Detec%ng temporal dependencies in the en.re result rela.on. b. Detec%ng temporal dependencies in individual agributes. a. Naïve dependencies b. Row matching

14 Contextual Analysis Detec.ng temporal dependencies D Name Account type balance Alice saving 0 Cathy checking 30,000 Q(D) Name Account type balance Context Alice saving 1000 [0,1000] Bob checking 500 [500] Cathy checking 2,000 [2000, 30000] Row changed Cell changed No change

15 Contextual Analysis Detec.ng temporal dependencies c. Quan%fying the effects of temporal dependencies T 1 - T n T n+1 Name Account type balance Alice saving 0 Bob checking 500 Name Account type balance Alice saving 1000 Bob checking 500 Q: SELECT * FROM D WHERE balance <1000 Name Account type balance Alice saving [0,1000] Bob checking 500

16 Prac.cal Temporal Dependency Detec.on 1. Monte- Carlo Approximate Instead of evalua%ng the query at every version, the query is only evaluated on a fixed set of sample versions S. The %me complexity of detec%ng temporal dependences in Q S scales linearly in S rather than in N, at the cost of result accuracy.

17 Prac.cal Temporal Dependency 2. Dynamic Analysis Detec.on R(a) and S(b) join by column B, the dynamic analysis of the join result is shown in (c).

18 Prac.cal Temporal Dependency 2. Dynamic Analysis Detec.on Name Account Type Balance ROWID XID Alice saving 100 r1 v1 Bob checking 200 r2 v1 Carol saving 5,000 r3 v1 UPDATE Acct SET Balance = Balance WHERE Name = 'Alice'; INSERT INTO Acct VALUES ('Carol', , saving,55); COMMIT; - - Creates v2 DELETE FROM Acct WHERE Name = 'Bob'; COMMIT; - - Creates v3 Name Account Type Balance ROWID XID Alice saving 10,000 r1 v2 Carol saving 5,000 r3 v1 Carol saving 55 r4 v2

19 Prac.cal Temporal Dependency Detec.on 2. Dynamic Analysis An interval encoding of the history of the rela.on Acct. Acct: Name Account Type Balance ROWID start end Alice saving 100 r1 - v2 Alice Saving 10,000 r1 v2 Bob checking 200 r2 - Carol saving 5,000 r3 - Carol saving 55 r4 v2

20 Prac.cal Temporal Dependency 2. Dynamic Analysis The result of query Q on Acct. Detec.on Original Query: SELECT Name, SUM(Balance) FROM ACCT GROUP BY Name Query rewrite Q(Acct): Name Balance start end Alice v2 Alice 10,000 v2 Bob v2 Carol 5,000 - v2 Carol 5,055 v2

21 Prac.cal Temporal Dependency Detec.on 2. Dynamic Analysis The impact assessment process result. Name Account Type Balance Confidence Alice saving [100,10000] 1 Bob checking [200,200] 0.75 Carol saving [5000,5055] 1

22 Prac.cal Temporal Dependency Detec.on 3. Analysis Composi.on D: Name Balance Time Q: SELECT * Alice 90 T1 Alice 120 T2 Alice 85 T5 Alice 85 T6 Precomputa%on: R: Name CONF Balancemin Balancemax Alice 2/ FROM D WHERE Balance >=120

23 Prac.cal Temporal Dependency 3. Analysis Composi.on R Query: SPJ Query rewri%ng Detec.on Name CONF Balancemin Balancemax Alice 2/ SELECT name,cmp_conf(balance >= 120)*R.CONF AS CONF,Balancemin,Balancemax FROM R WHERE Balance >= 120 Name CONF Balancemin Balancemax Alice (1/(120-85)*2/3)

24 Experiments Database: with temporal features ac%vated. Data: TPC- H 1GB with about 100,000 events from the synthe%c workload applied. These events simulate the day to day opera%on of a company. Queries: Qeury Class Output Size Raw Time TPCH1 SPA 4 rows 3s TPCH3 SPJA Many 1.5s TPCH9 SPJA Many 3.5s TPCH20(with changes) SPJ Many 5s

25 Experiments Query 1 Execution Time (s) Naive MonteCarlo-5 MonteCarlo-10 Dynamic Composable Number of Versions Considered Approximate algorithms outperform the exact ones. Dynamic analysis remains a constant factor (approximately 3-4 %mes) faster than the Naive strategy. Both approximate strategies show an expected near constant- %me performance.

26 Experiments Accuracy of the approximate algorithms on TPC- H Queries 1 Relative Error e-05 1e-06 CONF SUM_QTY SUM_BASE_PRICE SUM_DISC_PRICE SUM_CHARGE AVG_QTY AVG_PRICE AVG_DISC COUNT_ORDER Number of Versions Considered Relative Error e-05 1e-06 1e-07 CONF SUM_QTY SUM_BASE_PRICE SUM_DISC_PRICE SUM_CHARGE AVG_QTY AVG_PRICE AVG_DISC COUNT_ORDER 1e Number of Versions Considered 1e Composable Analysis 5- SampleMC 10- Sample MC Relative Error e-05 1e-06 1e-07 CONF SUM_QTY SUM_BASE_PRICE SUM_DISC_PRICE SUM_CHARGE AVG_QTY AVG_PRICE AVG_DISC COUNT_ORDER Number of Versions Considered Composable analysis is quite accurate, genera%ng es%mates to within four nines for most aggregate values. However, it generates extremely pessimis%c es%mates of confidence. This results in final confidence es%mates with as much as 50% error.

27 Experiments Query 3 Execution Time (s) Naive MonteCarlo-5 MonteCarlo-10 Dynamic Composable Number of Versions Considered Approximate methods show linear scaling. Dynamic analysis also shows linear scaling. due to the hierarchical nature of the query.

28 Experiments Query 9 Execution Time (s) Naive MonteCarlo-5 MonteCarlo-10 Dynamic Composable Number of Versions Considered Dynamic analysis is a constant factor faster than Naive analysis. There is a slight linear increase in the cost of the Monte Carlo approach as the number of versions considered approaches 10,000. and we suspect that this slight increase is due to the join width.

29 Experiments Accuracy of the approximate algorithms on TPC- H Queries 9 Relative Error e-05 1e-06 CONF SUM_PROFIT Number of Versions Considered Relative Error e-05 CONF SUM_PROFIT 1e Number of Versions Considered 1e-05 CONF SUM_PROFIT 1e Composable Analysis 5- Sample MC 10- Sample MC Relative Error Number of Versions Considered Accuracy increases as more versions are considered: this is an ar%fact of the actual confidence ge{ng closer to the pessimis%c bound. The monte- carlo methods fared signicantly be er at es%ma%ng the aggregate values computa%ons. However, they did not iden%fy some output groups, crea%ng an extremely high confidence error.

30 Summary What is Context? Prac.cal Temporal Dependency Detec.on a. Monte- Carlo Approximate b. Dynamic Analysis c. Analysis Composi%on