Discovering Robust Knowledge from Databases that Change

Chun-Nan Hsu Craig A. Knoblock Arizona State University University of Southern California Journal of Data Mining and Knowledge Discovery, Volume 2, 1998 Discovering Robust Knowledge from Databases that Change Presented & Edited by: Danielle Steimke CS332/295 Data Mining – Spring 2014

Outline Introduction Preliminaries Robustness of Knowledge Applying Robustness in Knowledge Discovery Conclusions

Introduction: Importance Real world databases are dynamic. Changing information (updates, deletions) may make current rules incorrect How can we detect, check & update these inconsistent rules without high maintenance costs? Use robustness as a measure of how likely knowledge is to be consistent after database changes

Introduction: BASIL BASIL rule discovery system – discovers semantic rules that optimize performance Many rules may be invalid after DB changes Use robustness estimation to guide data mining using a rule pruning approach Search for pruning pattern that yields highly robust rules

Introduction: Applications of Robustness Estimation Minimize the maintenance cost of inconsistent rules in the presence of database changes. When an inconsistent rule is detected, a system may: remove the rule: repeatedly invoke discovery system repair the rule: repaired frequently after data changes Apply to guide the data mining and evaluation of semantic rules: In data mining stage prune antecedents of a discovered rule to increase its robustness. In evaluation stage, eliminate rules with robustness values below a threshold.

Introduction: Robustness Comparison Robustness vs. Support: Support - the probability that a data instance satisfies a rule. Robustness - probability that an entire database state is consistent with a rule. Robustness vs. Predictive Accuracy: Predictive - the probability that knowledge is consistent with randomly selected unseen data. The difference is significant in databases that are interpreted using the closed-world assumption(CWA).

Introduction: Closed-world Assumption Information that is not explicitly present in the database is taken to be false. Closed-world databases are widely used in relational db’s, deductive db’s and rule-based information systems because of The characteristics of application domains. The limitation of the representation systems. Databases change by deletions, insertions and updates, and in a close-world database, they may affect the validity of a rule. Closed-world data tends to be dynamic, important for knowledge discovery systems to handle dynamic and closed world data.

Important Definitions Database state at time t: the collection of the instances presents in the database at time t. Database Literals vs. Built-In Literals Range Rule vs. Relational Rule Consistent rule: given a database state,if all variable instantiations that satisfy the antecedents of the rule also satisfy the consequent of the rule.

First Definition of Robustness Robust rule: does not become inconsistent (invalid) after database changes Definition 1: (Robustness for all states) Given a rule r, let D be the event that a database is in a state that is consistent with r.

First Definition Continued Two problems with estimate Treats all database states as if they are equally probable Number of database states is large, even in small databases

Second Definition of Robustness Robustness for accessible states: Given a rule r, and a database in a state d, in whichris consistent. Let t denote the event of performing a transaction on d that results in new database states inconsistent withr. The robustness ofr in accessible states from the current state d is defined as

Definitions of Robustness (cont.) • If all transactions are equally probable, then • The robustness of a rule could be different in different database states. E.g.: Suppose there are two db states d1 and d2 of a given db. To reach a state inconsistent with r, we need to delete 10 tuples in d1 and only 1 tuple in d2. Robust(r|d1) > Robust(r|d2)

Estimate Robustness Estimate the probabilities of data changes, rather than the number of possible database states. Estimate the robustness of a rule based on the probability of transactions that may invalidate the rule. Decompose data changing transactions and estimate their probabilities using the Laplace law of succession.

Estimate Robustness (cont.) • Laplace law of Succession: Given a repeatable experiment with an outcome of one of any of k classes. Suppose we have conducted this experimentn times,r of which have resulted in some outcome C, in which we are interested. The probability that the outcome of the next experiment will be C can be estimated as

Decomposition of Transactions To estimate Pr(t|d) Use Bayesian network model to decompose the transaction and estimate local probabilities first X3: on which tuples? X1: type of transaction? X2: on which relation? X5: what new attribute value? X4: on which attributes?

Estimating Robustness Estimation accuracy depends on available information. However, even given only database schemas, the method can still come up with some estimates. How to derive transactions that invalidate an arbitrary logic statement? Most knowledge discovery systems have strong restrictions on the syntax of discovered knowledge. Therefore the invalidating transactions can be manually generalized into a small sets of transaction templates, as well as templates of probability estimates for robustness estimation.

Applying Robustness in Knowledge Discovery A rule pruning algorithm which can increase the robustness and applicability of machine discovered rules by pruning their antecedent literals. Specification of rule pruning problem: Take a machine-generated rule as input, which is consistent with a database but potentially overly-specific, and remove antecedent literals of the rule so that it remains consistent but is short and robust. Basic idea of pruning algorithm: To search for a subset of antecedent literals to remove until any further removal will yield an inconsistent rule. However the search space can be exponentially large with respect to the number of literals in rule.  A beam-search algorithm was presented to trim the search space.

Antecedent pruning algorithm Pruning rule antecedents 1. INPUTR = rules (initially the rule to be pruned),B= beam size; 2. LET O= results;( initially empty); 3. WHILE (R is not empty) DO 4. Move the first rule r in R to O; 5. Prune r, LET R’ = resulting rules; 6. Remove visited dangling or inconsistent rules in R’; 7. Estimate and sort on the robustness of rules in R’; 8. Retain top B rules in R’ and remove the rest; 9. Merge sorted R’ into R in sorted order of the robustness; 10. RETURN O ;

Pruning Algorithm Beam-search algorithm Apply the robustness estimation approach to estimate the robustness of a partially pruned rule and guide the pruning search. Two properties to optimize: robustness and length. For each set of equally short rules, the algorithm searches for the rule that is as robust as possible while still being consistent.

Experimental Results Experiment setups: Use the rule discovery system BASIL to derive rules from two large ORACLE relations databases BASIL part of SIMS information mediator developed by authors Synthesize 123 sample transactions to represent possible transactions of the experimental databases (27 updates, 29 deletions and 67 insertions) Steps of the experiments: Train BASIL to discover a set of rules and estimate their robustness, generates 355 rules Use another set of 202 sample transactions to assist the robustness estimation. Apply the set of 123 transactions to the two relational database and check consistency of all 355 rules

Conclusions Formalize the notion of robustness of a rule r in a given database state d Estimate probabilities of rule invalidating transactions Decomposes the probability of a transactions into local probabilities that can be estimated using Laplace law. No need to provide additional information for the estimation. Apply in a rule discovery system Pruning antecedents of a discovered rule so that the rule will be highly robust and widely applicable. Beam-search algorithm Conduct empirical experiments to demonstrate the algorithm.

Real-world Applications Face recognition software Envirionment is the “data set” – constantly changing Rules must resist variability in lighting and pose Systems prognostics Data set continuously updated with diagnostic or calibration information Predictive failure rules used to dictate system resource planning

First Exam Question Why is finding robust rules significant?

First Question - Answer Real world databases tend to be dynamic Changing information – updates and deletions, rather than just additions – could invalidate current rules Continually checking and updating rules may incur high maintenance costs, especially for large databases Robustness measures how likely the knowledge found will be consistent after changes to the database

Second Exam Question Compare and Contrast Robustness estimations with support and predictive accuracy

Second Question - Answer Robustness is the probability that an entire database state is consistent with a rule, while support is the probability that a specific data instance satisfies a rule. Predictive Accuracy is the probability that knowledge is consistent with randomly selected unseen data. This is significant in closed world databases, where data tends to be dynamic.

Third Exam Question Why are transaction classes mutually exclusive when calculating robustness?

Third Question – Answer Transaction classes are mutually exclusive so that no transaction class covers another because for any two classes of transactions t_a and t_b, if t_a covers t_b, then Pr(t_a⋀t_b)=Pr(t_a) and it is redundant to consider t_b.

Discovering Robust Knowledge from Databases that Change