Information Management course
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1 Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015
2 Practical informations: Teacher: Alberto Ceselli Course weekly schedule: Tutoring: Tuesday (3 nord) Wednesday (3 nord) Anytime after the lectures Contact me by whenever you can find me in my office Homepage: homes.di.unimi.it/ceselli/im
3 Practical Informations (2) Reference book: J. Han, M. Kamber (J. Pei), Data Mining: concepts and Techniques, 2nd (3rd) edition, 2006 (2011) Exam: Development of a project + project discussion + general check on theory
4 Why Information Management? Let's start from a user perspective: I have a lot of data (queries on Google) I'm interested in making a decision about my future: weather to follow or not the Information Management course Can I extract knowledge from data? DMBS Data warehouse Big data Data analytics And that's only statistical evaluation we're interested in analytics!
5 Advanced analysis example: disjunctive mapping Instead of searching for rules like A and B C search for rules like A or B C
6 Evolution of Sciences: New Data Science Era Before 1600: Empirical science s: Theoretical science Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. 1950s-1990s: Computational science Over the last 50 years, most disciplines have grown a third, computational branch (i.e. empirical, theoretical, and computational branches) Computational Science: what can I do if I am not able to find closed-form solutions for complex mathematical models? 1990-now: Data science The flood of data from new scientific instruments and simulations The ability to economically store and manage petabytes of data online Computing grids that make all these archives accessible Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes Data analytics is a major new challenge! Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov
7 Why Information Management? A set of consolidated (still improving) techniques in data management: Data Mining
8 Data Mining: Concepts and Techniques (3rd ed.) Chapter 1 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2012 Han, Kamber & Pei. All rights reserved. 9
9 What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative names Data mining: a misnomer? Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, big data analytics, etc. Watch out: Is everything data mining? Simple search and query processing (Deductive) expert systems 10
10 Association and Correlation Analysis Frequent patterns (or frequent itemsets) What items are frequently purchased together? Association, correlation vs. causality A typical association rule Diaper Beer [0.5%, 75%] (support, confidence) Are strongly associated items also strongly correlated? How to mine such patterns and rules efficiently in large datasets? How to use such patterns for classification, clustering, and other applications? 11
11 Classification Classification and label prediction Construct models based on some training examples Describe and distinguish classes or concepts for prediction Predict some unknown class labels Typical methods E.g., classify countries based on (climate), or classify cars based on (gas mileage) Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, Typical applications: Credit card fraud detection, direct marketing, classifying stars, diseases, web-pages, 12
12 Cluster Analysis Unsupervised learning (i.e., Class label is unknown) Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns Principle: Maximizing intra-class similarity & minimizing interclass similarity Many methods and applications 13
13 Outlier Analysis Outlier analysis Outlier: A data object that does not comply with the general behavior of the data Noise or exception? One person s garbage could be another person s treasure Methods: by product of clustering or regression analysis, Useful in fraud detection, rare events analysis 14
14 Time and Ordering: Sequential Pattern, Trend and Evolution Analysis Sequence, trend and evolution analysis Trend, time-series, and deviation analysis: e.g., regression and value prediction Sequential pattern mining e.g., first buy digital camera, then buy large SD memory cards Periodicity analysis Motifs and biological sequence analysis Approximate and consecutive motifs Similarity-based analysis Mining data streams Ordered, time-varying, potentially infinite, data streams 15
15 Structure and Network Analysis Graph mining Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) Information network analysis Social networks: actors (objects, nodes) and relationships (edges) e.g., author networks in CS, terrorist networks Multiple heterogeneous networks A person could be multiple information networks: friends, family, classmates, Links carry a lot of semantic information: Link mining Web mining Web is a big information network: from PageRank to Google Analysis of Web information networks (Web community discovery, opinion mining, usage mining, ) 16
16 Why Information Management? Two arising issues in data management: Big Data + Analytics
17 Bottom Issue: Big Data
18 Big Data: Volume
19 Big Data: Velocity
20 Big Data: Variety
21 Big Data: Veracity
22 Top Issue: Analytics (SAS)
23 Top Issue: Analytics (IBM)
24 Top Issue: Analytics (IBM)
25 Data Mining in Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques End User Business Analyst Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems Data Analyst DBA 26
26 Data analytics: Confluence of Multiple Disciplines Machine Learning Applications Pattern Recognition Statistics Data Mgm & Analytics Algorithms Operations Research Visualization High-Performance Computing Database Technology 27
27 Summarizing: why Confluence of Multiple Disciplines? Tremendous amount of data High-dimensionality of data Micro-array may have tens of thousands of dimensions High complexity of data Algorithms must be highly scalable to handle such as terabytes of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social networks and multi-linked data Heterogeneous databases and legacy databases Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations New and sophisticated applications 28
28 The Data Journey Data Data Collection Collection Quality Quality check check Data Data Warehousing Warehousing Analytics Analytics Data Data Preprocessing Preprocessing Visualization Visualization
29 The Data Journey A new professional profile: the data steward
30 Information Management course syllabus Know your data: data objects and attribute types, basic statistical descriptions of data, measuring data proximity. Data preprocessing: the quality of data; data cleaning, integration; samples reduction. Dimensionality reduction: Principal Component Analysis; Feature selection algorithms On Line Analytical Processing and Data Warehousing Mining frequent patterns, ideas, algorithms and pattern evaluation measures Classification: basic concepts and ideas; decision tree induction models and algorithms. Bayesian classification; Bayesian Belief Networks. Support Vector Machines Clustering: partitioning, hierarchical and density-based methods; evaluation of clustering methods (Time series analysis) (Data mining in networks & graphs / fraud detection)
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