Mapping Maintenance for Data Integration Systems
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1 Mapping Maintenance for Data Integration Systems R. McCann, B. AlShebli, Q. Le, H. Nguyen, L. Vu, A. Doan University of Illinois at Urbana-Champaign VLDB 2005 Laurent Charlin October 26, 2005 Mapping Maintenance for Data Integration Systems October 26, / 23 Outline Introduction Problem Definition Previous Work in the domain & Background information, automatic mapping verification system Sensor Ensemble Perturber Multi-Source Trainer Filter Results from the paper Critique & Discussion Mapping Maintenance for Data Integration Systems October 26, / 23 Overall Problem Definition They Assume a typical schema integration instance Find homes under $300K Mediated Schema Source Schema Source Schema Source Schema Wrapper Wrapper Wrapper homeseekers.com yahoo.com windermere.com Mapping Maintenance for Data Integration Systems October 26, / 23
2 Statement Problem Definition They want to solve the Semantic mapping verification problem (ie: answer the question, Is a given mapping broken?) Assume that the Semantic mapping has been done Motivation: They have found that the dominating cost is often the mapping maintenance (detect and repair). Mapping Maintenance for Data Integration Systems October 26, / 23 Background Previous Work The authors have a strong AI (machine learning) background They are very active in this domain From Doan s Ph.D. thesis First, it introduced machine learning as an indispensable component of matching solutions. Second, it articulated a multi-component, highly extensible architecture for schema matching. Third, it showed how to learn from past matching efforts (to improve accuracy of subsequent matching tasks). Mapping Maintenance for Data Integration Systems October 26, / 23 Some Perspective Previous Work First, Regression tester, relies on formating regularities Kushmerick (2000), Rapture system, syntaxic only Could be 1 sensor in the current system Lerman et al. (2003), syntaxic measure as well (results in the experiments section). Learned structural information Positive Data only Mapping Maintenance for Data Integration Systems October 26, / 23
3 Some Perspective Previous Work Activity monitoring (for example, Fawcett et al., 99) It might be formulated as a stream problem Data is continuously arriving from querying the sources You even have some control on the stream since you re controlling the queries. Mapping Maintenance for Data Integration Systems October 26, / 23 Previous Work Typical Machine Learning - Online learning algorithm Given a m experts (sensors) At each time step (iteration) the sensors predict scorej [0, 1] the learner, based on all the scorej predicts score cum [0, 1] compare scorecum with the actual label of the example (update the sensor weights based on this). In verification (testing): You simply predict with the learned weights (this is taken from Robert Schapire s lectures) Mapping Maintenance for Data Integration Systems October 26, / 23 Back to the current problem M S semantic mappings description cost Q G Reformulator T G global results description cost townhome 185,000 ranch 260,000 category price Q S Query Interface I S Wra p p e r T S structured source results category price townhome 185,000 ranch 260,000 Source S HTML results H S Mapping Maintenance for Data Integration Systems October 26, / 23
4 1 - The Sensors Computational modules which capture specific characteristics of a source S Idea 1. Train them on data from S 2. Deploy them to monitor the data returned by queries Mapping Maintenance for Data Integration Systems October 26, / The Sensors Generate examples from querying valid semantic mappings Two types of parameters to learn The parameters of each sensor (Gaussian mean and variance) The weight of the sensors (in the Winnown algorithm) Mapping Maintenance for Data Integration Systems October 26, / Winnow Algorithm Final classifier is given by m vote valid = w i score i vote invalid = i m w i (1 score i ) i Mapping Maintenance score for Data Integration = vote Systems /(vote + votevalid) October 26, / 23
5 1 - The Sensor Types Value Sensors Monitor features of attributes Data modeled according to a Gaussian distribution Density Scoring scores = 1 P(v) Normalized Density scoring scores = Pr[P(v ) P(v)] Trend Sensors Work much like the Value sensors Layout Sensors Monitors the HTML layouts Constraint Sensors Monitors pre-defined attribute constraints Mapping Maintenance for Data Integration Systems October 26, / Perturbation Problem: No Negative Example Solution: Generate negative examples from current source (S) data Corrolary: They are also trying to generate more diversified examples They Simulate the following situations Change in the Source Query Interface Change Source Data Change the Presentation Format In training they incorporate this new data (both positive and negative) into the examples R. Mapping Maintenance for Data Integration Systems October 26, / Perturbation The addition of positive (invalid data) changed the way the score is calculted It s due to the fact that they use Gaussian modeling of the data (they cannot incorporate both positive and negative examples in their distributions). They have two Gaussians score cum = score /(score + score + ) Mapping Maintenance for Data Integration Systems October 26, / 23
6 3 - Multi-Source training Usually you have to train on a single source S What about using other sources S from the same domain which have equivalent attributes Example (two attributes which are tied by the semantic schema): S: price $185, 000 S : amount 185, 000USD Mapping Maintenance for Data Integration Systems October 26, / Filtering Motivation : Have to find a balance between false positve and false negative (it cannot be attained by changing the value of the threshold θ). 3 filters Each as the ability to silence attribute If some are not silenced after passing the filters then you raise an alarm Mapping Maintenance for Data Integration Systems October 26, / Filtering 1. Syntaxic Recognizer (much in the style of previous papers on the subject) 2. Exploiting External Sources Trains a new sensor on newly acquired data from a different source Lets you learn from other previously broken mappings 3. Learning from the web (Google is your friend!) Is 185,000 USD a cost? Search for: ,000 USD 3.2 cost 185,000 USD If the ratio is high enough, then it s valid (ie: silence the attribute) This is the most semantic it gets Mapping Maintenance for Data Integration Systems October 26, / 23
7 Results Experiments 1 Sensor Ensemble (D) Sensor Ensemble (D) + Perturbation Sensor Ensemble (D) + Perturbation + Multi-Src Train Sensor Ensemble (D) + Perturbation + Multi-Src Train + Filtering 0.9 F Flights Books Researchers Real Estate Inventory Courses Mapping Maintenance for Data Integration Systems October 26, / 23 Results Experiments Domain Flights Books Researchers Real Estate Inventory Courses Lerman System P / R 0.81 / / / / / / 0.94 F Sensor Ensemble (D) P / R / / / / / / 0.88 F Sensor Ensemble (ND) P / R / / / / / / 0.87 F Mapping Maintenance for Data Integration Systems October 26, / 23 Discussion Conclusion They improve previous work by Broader collection of evidences The ensemble of sensors allow to use these evidences Weighted combining of sensors They still have to improve Unrecognized formats (not seen in training and not on the web) Mixed same type attributes when they were switched Mapping Maintenance for Data Integration Systems October 26, / 23
8 Final remarks Conclusion Why not use a more powerful meta-learning algorithm (ie: AdaBoost (Schapire 90s))? How far can we push the stream analogy? Why does (D) perform as well as (ND)? Mapping Maintenance for Data Integration Systems October 26, / 23 Final remarks Conclusion Previous work do mostly syntaxic analysis This paper does it a little better They have the same fundamental problems as other papers Very adhoc sensors Needs to train on every source independently Filtering is a good idea but it needs to be pushed further Next step, try to understand semantically the query returns AI techniques would become even more important Mapping Maintenance for Data Integration Systems October 26, / 23
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