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מבוא ל BI. Automated Decision-Making Framework. BI (לפי ויקיפדיה). http://he.wikipedia.org/wiki/%D7%91%D7%99%D7%A0%D7%94_%D7%A2%D7%A1%D7%A7%D7%99%D7%AA תוכן עניינים 1  היסטוריה 2 תהליך העבודה 3 מחסן נתונים ו- BI 4 עיבוד אנליטי מקוון (OLAP ) 5  כריית מידע (כל שיטות הלמידה שלמדנו)

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slide3
BI(לפי ויקיפדיה)
  • http://he.wikipedia.org/wiki/%D7%91%D7%99%D7%A0%D7%94_%D7%A2%D7%A1%D7%A7%D7%99%D7%AA

תוכן עניינים

  • 1 היסטוריה
  • 2 תהליך העבודה
  • 3 מחסן נתונים ו-BI
  • 4עיבוד אנליטי מקוון (OLAP)
  • 5 כריית מידע (כל שיטות הלמידה שלמדנו)
  • 6 בינה עסקית תפעולית
  • 7 שימושים עיקריים
  • 8 מוצרי BI
slide4
היסטוריה של DSS

Classical Definitions of DSS

  • Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems" - Gorry and Scott-Morton, 1971
  • Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems - Keen and Scott-Morton, 1978
types of dss
Types of DSS
  • Two major types:
    • Model-oriented DSS
    • Data-oriented DSS
  • Evolution of DSS into Business Intelligence
    • Use of DSS moved from specialist to managers, and then whomever, whenever, wherever
    • Enabling tools like OLAP, data warehousing, data mining, intelligent systems, delivered via Web technology have collectively led to the term “business intelligence” (BI) and “business analytics”
slide6
מויקיפדיה...

החל מאמצע שנות ה-2000 קיימים כלים חדשים לבינה עסקית בתפיסה הנקראת Business Intelligence 2.0 ‏(BI 2.0), המאפשרים ביצוע שאילתות על ידי עובדים על נתוני הארגון בזמן אמיתי. המושג BI 2.0 נטבע בהקבלה למושג Web 2.0משום שעיבודים מסוג זה הם בתפיסה של דפדפן בסביבת Web. כלי BI 2.0 מאפשרים דיווחים דינמיים יותר מהדיווחים הסטטיים שאפיינו כלים מדור קודם. בסיס חשוב לעיבודים מסוג זה הוא השימוש ב-SOA, שבא ביחד עם שימוש במוצרי תָ‏וְוכָ‏ה (Middleware) גמישים יותר ושימוש בתקנים להעברת מידע.

Service Oriented Architecture =SOA

dss description
DSS Description
  • DSS application

A DSS program built for a specific purpose (e.g., a scheduling system for a specific company)

  • Business intelligence (BI)

A conceptual framework for decision support. It combines architecture, databases (or data warehouses), analytical tools, and applications

business intelligence bi
Business Intelligence (BI)
  • BI is an evolution of decision support concepts over time.
    • Meaning of EIS/DSS…
      • Then: Executive Information System
      • Now: Everybody’s Information System (BI)
  • BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities.
  • The term BI emerged from industry apps.
the architecture of bi
The Architecture of BI
  • A BI system has four major components
    • a data warehouse, with its source data
    • business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse;
    • business performance management (BPM) for monitoring and analyzing performance
    • a user interface (e.g., dashboard)
    • בשנים האחרונות תפס הנושא של בינה עסקית מקום מרכזי במערכות המידע. הגידול הרב במידע הנצבר במערכות ממוחשבות מחייב הצגה וריכוז של נתונים רלוונטיים על מנת שלמידע תהיה משמעות. אחד הביטויים לחשיבות התחום הוא רכישת חברות בולטות המתמחות בתחום על ידי חברות תוכנה גדולות
learning objectives
Learning Objectives
  • Explain data integration and the extraction, transformation, and load (ETL) processes
  • Describe real-time (a.k.a. right-time and/or active) data warehousing
  • Understand data warehouse administration and security issues
stage 1 data warehouse
Stage 1: Data Warehouse
  • A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
  • “The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”
data integration and the extraction transformation and load etl process
Data Integration and the Extraction, Transformation, and Load (ETL) Process

Extraction, transformation, and load (ETL)

data mart
Data Mart

A departmental data warehouse that stores only relevant data

  • Dependent data mart

A subset that is created directly from a data warehouse

  • Independent data mart

A small data warehouse designed for a strategic business unit or a department

slide18
OLAP

Slicing Operations on a Simple Tree-Dimensional

Data Cube

slide21
כריית מידע
  • סיווג (שווה או לא שווה ל...)
    • להלוות כסף, להשקיע בתחום, לפחות סניף חדש
  • ניתוח אשכולות (Clustering)
    • כמה סוגי לקוחות יש? מה מאחד אותם?
  • ניתוח רגרסיה
    • כמה נרוויח, אופטימיזציה
slide22
סוגי מידע
  • כריית מידע מנתונים
    • היותר "פשוט"
  • כריית מידע מטקסטים
    • INFORMATION RETRIEVAL
    • TREND ANALYSIS, SENTIMENT ANALYSIS
static and dynamic models
Static and Dynamic Models
  • Static Analysis
    • Single snapshot of the situation
    • Single interval
    • Steady state
  • Dynamic Analysis
    • Dynamic models
    • Evaluate scenarios that change over time
    • Time dependent
    • Represents trends and patterns over time
    • More realistic: Extends static models
decision analysis a few alternatives
Decision Analysis: A Few Alternatives

Single Goal Situations

  • Decision trees
    • Graphical representation of relationships
    • Multiple criteria approach
    • Demonstrates complex relationships
    • Cumbersome, if many alternatives exists
decision tables
Decision Tables
  • Investment example
  • One goal: maximize the yield after one year
  • Yield depends on the status of the economy

(the state of nature)

    • Solid growth
    • Stagnation
    • Inflation
investment example possible situations
Investment Example: Possible Situations

1. If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5%

2. If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5%

3. If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5%

optimization via mathematical programming
Optimization via Mathematical Programming
  • Mathematical Programming

A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal

  • Optimal solution: The best possible solution to a modeled problem
    • Linear programming (LP): A mathematical model for the optimal solution of resource allocation problems. All the relationships are linear
lp problem characteristics
LP Problem Characteristics

1. Limited quantity of economic resources

2. Resources are used in the production of products or services

3. Two or more ways (solutions, programs) to use the resources

4. Each activity (product or service) yields a return in terms of the goal

5. Allocation is usually restricted by constraints

linear programming steps
Linear Programming Steps
  • 1. Identify the …
    • Decision variables
    • Objective function
    • Objective function coefficients
    • Constraints
      • Capacities / Demands
  • 2. Represent the model
    • LINDO: Write mathematical formulation
    • EXCEL: Input data into specific cells in Excel
  • 3. Run the model and observe the results

Line

lp example
LP Example

The Product-Mix Linear Programming Model

  • MBI Corporation
  • Decision: How many computers to build next month?
  • Two types of mainframe computers: CC7 and CC8
  • Constraints: Labor limits, Materials limit, Marketing lower limitsCC7CC8RelLimitLabor (days) 300 500 <= 200,000 /mo Materials ($) 10,000 15,000 <= 8,000,000 /mo Units 1 >= 100 Units 1 >= 200 Profit ($) 8,000 12,000 Max Objective: Maximize Total Profit / Month
sensitivity what if and goal seeking analysis
Sensitivity, What-if, and Goal Seeking Analysis
  • Sensitivity
    • Assesses impact of change in inputs on outputs
    • Eliminates or reduces variables
    • Can be automatic or trial and error
  • What-if
    • Assesses solutions based on changes in variables or assumptions (scenario analysis)
  • Goal seeking
    • Backwards approach, starts with goal
    • Determines values of inputs needed to achieve goal
    • Example is break-even point determination
heuristic programming
Heuristic Programming
  • Cuts the search space
  • Gets satisfactory solutions more quickly and less expensively
  • Finds good enough feasible solutions to very complex problems
  • Heuristics can be
    • Quantitative
    • Qualitative (in ES)
  • Traveling Salesman Problem >>>
traveling salesman problem
Traveling Salesman Problem
  • What is it?
    • A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible route
    • Total number of unique routes (TNUR):

TNUR = (1/2) (Number of Cities – 1)!

Number of CitiesTNUR

5 12

6 60

9 20,160

20 1.22 1018

when to use heuristics
When to Use Heuristics

When to Use Heuristics

  • Inexact or limited input data
  • Complex reality
  • Reliable, exact algorithm not available
  • Computation time excessive
  • For making quick decisions

Limitations of Heuristics

  • Cannot guarantee an optimal solution
modern heuristic methods
Modern Heuristic Methods
  • Tabu search
    • Intelligent search algorithm
  • Genetic algorithms
    • Survival of the fittest
  • Simulated annealing
    • Analogy to Thermodynamics
simulation
Simulation
  • Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system
  • Frequently used in DSS tools
major characteristics of simulation
Major Characteristics of Simulation
  • Imitates reality and capture its richness
  • Technique for conducting experiments
  • Descriptive, not normative tool
  • Often to “solve” very complex problems

Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques

advantages of simulation
Advantages of Simulation
  • The theory is fairly straightforward
  • Great deal of time compression
  • Experiment with different alternatives
  • The model reflects manager’s perspective
  • Can handle wide variety of problem types
  • Can include the real complexities of problems
  • Produces important performance measures
  • Often it is the only DSS modeling tool for non-structured problems
limitations of simulation
Limitations of Simulation
  • Cannot guarantee an optimal solution
  • Slow and costly construction process
  • Cannot transfer solutions and inferences to solve other problems (problem specific)
  • So easy to explain/sell to managers, may lead overlooking analytical solutions
  • Software may require special skills
simulation types
Simulation Types
  • Stochastic vs. Deterministic Simulation
    • In stochastic simulations: We use distributions (Discrete or Continuous probability distributions)
  • Time-dependent vs. Time-independent Simulation
    • Time independent stochastic simulation via Monte Carlo technique (X = A + B)
  • Discrete event vs. Continuous simulation
  • Steady State vs. Transient Simulation
  • Simulation Implementation
    • Visual simulation
    • Object-oriented simulation
data mining methods classification
Data Mining Methods: Classification
  • Most frequently used DM method
  • Part of the machine-learning family
  • Employ supervised learning
  • Learn from past data, classify new data
  • The output variable is categorical (nominal or ordinal) in nature
  • Classification versus regression?
  • Classification versus clustering?
assessment methods for classification
Assessment Methods for Classification
  • Predictive accuracy
    • Hit rate
  • Speed
    • Model building; predicting
  • Robustness
  • Scalability
  • Interpretability
    • Transparency, explainability
accuracy of classification models
Accuracy of Classification Models
  • In classification problems, the primary source for accuracy estimation is the confusion matrix
estimation methodologies for classification
Estimation Methodologies for Classification
  • Simple split (or holdout or test sample estimation)
    • Split the data into 2 mutually exclusive sets training (~70%) and testing (30%)
estimation methodologies for classification1
Estimation Methodologies for Classification
  • k-Fold Cross Validation (rotation estimation)
    • Split the data into k mutually exclusive subsets
    • Use each subset as testing while using the rest of the subsets as training
    • Repeat the experimentation for k times
    • Aggregate the test results for true estimation of prediction accuracy training
  • Other estimation methodologies
    • Leave-one-out, bootstrapping, jackknifing
    • Area under the ROC curve
classification techniques
Classification Techniques
  • Decision tree analysis
  • Statistical analysis
  • Neural networks
  • Support vector machines
  • Case-based reasoning
  • Bayesian classifiers
  • Genetic algorithms
  • Rough sets
decision trees
Decision Trees
  • Employs the divide and conquer method
  • Recursively divides a training set until each division consists of examples from one class
    • Create a root node and assign all of the training data to it
    • Select the best splitting attribute
    • Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split
    • Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached
decision trees1
Decision Trees
  • DT algorithms mainly differ on
    • Splitting criteria
      • Which variable to split first?
      • What values to use to split?
      • How many splits to form for each node?
    • Stopping criteria
      • When to stop building the tree
    • Pruning (generalization method)
      • Pre-pruning versus post-pruning
  • Most popular DT algorithms include
    • ID3, C4.5, C5; CART; CHAID; M5
cluster analysis for data mining
Cluster Analysis for Data Mining
  • k-Means Clustering Algorithm
    • k : pre-determined number of clusters
    • Algorithm (Step 0: determine value of k)

Step 1: Randomly generate k random points as initial cluster centers

Step 2: Assign each point to the nearest cluster center

Step 3: Re-compute the new cluster centers

Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable)

data mining myths
Data Mining Myths
  • Data mining …
    • provides instant solutions/predictions
    • is not yet viable for business applications
    • requires a separate, dedicated database
    • can only be done by those with advanced degrees
    • is only for large firms that have lots of customer data
    • is another name for the good-old statistics
common data mining mistakes
Common Data Mining Mistakes
  • Selecting the wrong problem for data mining
  • Ignoring what your sponsor thinks data mining is and what it really can/cannot do
  • Not leaving insufficient time for data acquisition, selection and preparation
  • Looking only at aggregated results and not at individual records/predictions
  • Being sloppy about keeping track of the data mining procedure and results
common data mining mistakes1
Common Data Mining Mistakes
  • Ignoring suspicious (good or bad) findings and quickly moving on
  • Running mining algorithms repeatedly and blindly, without thinking about the next stage
  • Naively believing everything you are told about the data
  • Naively believing everything you are told about your own data mining analysis
  • Measuring your results differently from the way your sponsor measures them
text mining application area
Text Mining Application Area
  • Information extraction
  • Topic tracking
  • Summarization
  • Categorization
  • Clustering
  • Concept linking
  • Question answering
text mining terminology
Text Mining Terminology
  • Unstructured or semistructured data
  • Corpus (and corpora)
  • Terms
  • Concepts
  • Stemming
  • Stop words (and include words)
  • Synonyms (and polysemes)
  • Tokenizing
text mining terminology1
Text Mining Terminology
  • Term dictionary
  • Word frequency
  • Part-of-speech tagging
  • Morphology
  • Term-by-document matrix
    • Occurrence matrix
  • Singular value decomposition
    • Latent semantic indexing
natural language processing nlp
Natural Language Processing (NLP)
  • Structuring a collection of text
    • Old approach: bag-of-words
    • New approach: natural language processing
  • NLP is …
    • a very important concept in text mining
    • a subfield of artificial intelligence and computational linguistics
    • the studies of "understanding" the natural human language
  • Syntax versus semantics based text mining
natural language processing nlp1
Natural Language Processing (NLP)
  • Challenges in NLP
    • Part-of-speech tagging
    • Text segmentation
    • Word sense disambiguation
    • Syntax ambiguity
    • Imperfect or irregular input
    • Speech acts
  • Dream of AI community
    • to have algorithms that are capable of automatically reading and obtaining knowledge from text
nlp task categories
NLP Task Categories
  • Information retrieval
  • Information extraction
  • Named-entity recognition
  • Question answering
  • Automatic summarization
  • Natural language generation and understanding
  • Machine translation
  • Foreign language reading and writing
  • Speech recognition
  • Text proofing
  • Optical character recognition
text mining applications
Text Mining Applications
  • Marketing applications
    • Enables better CRM
  • Security applications
    • ECHELON, OASIS
    • Deception detection (…)
  • Medicine and biology
    • Literature-based gene identification (…)
  • Academic applications
    • Research stream analysis
web mining success stories
Web Mining Success Stories
  • Amazon.com, Ask.com, Scholastic.com, …
  • Website Optimization Ecosystem
bpm versus bi
BPM versus BI
  • BPM is an outgrowth of BI and incorporates many of its technologies, applications, and techniques.
    • The same companies market and sell them.
    • BI has evolved so that many of the original differences between the two no longer exist (e.g., BI used to be focused on departmental rather than enterprise-wide projects).
    • BI is a crucial element of BPM.
  • BPM = BI + Planning (a unified solution)
performance measurement kpis and operational metrics
Performance Measurement KPIs and Operational Metrics
  • Key performance indicator (KPI)

A KPI represents a strategic objective and metric that measures performance against a goal

  • Distinguishing features of KPIs
  • Strategy
  • Targets
  • Ranges
  • Encodings
  • Time frames
  • Benchmarks
performance measurement
Performance Measurement
  • Key performance indicator (KPI)

Outcome KPIsvs. Driver KPIs

(lagging indicators (leading indicators

e.g., revenues) e.g., sales leads)

  • Operational areas covered by driver KPIs
    • Customer performance
    • Service performance
    • Sales operations
    • Sales plan/forecast
bpm methodologies
BPM Methodologies
  • The meaning of “balance”
    • BSC is designed to overcome the limitations of systems that are financially focused
    • Nonfinancial objectives fall into one of three perspectives:
      • Customer
      • Internal business process
      • Learning and growth
bpm methodologies1
BPM Methodologies
  • In BSC, the term “balance” arises because the combined set of measures are supposed to encompass indicators that are:
    • Financial and nonfinancial
    • Leading and lagging
    • Internal and external
    • Quantitative and qualitative
    • Short term and long term
bpm methodologies2
BPM Methodologies

Strategy map

A visual display that delineates the relationships among the key organizational objectives for all four BSC perspectives

performance dashboards
Performance Dashboards
  • Dashboards and scorecards both provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily explored
performance dashboards2
Performance Dashboards
  • Dashboards versus scorecards
    • Performance dashboards

Visual display used to monitor operational performance (free form)

    • Performance scorecards

Visual display used to chart progress against strategic and tactical goals and targets (predetermined measures)