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Thursday, January 5, 2023
Big Data Analytics Lifecycle
Mean Time Failure and other Formulas
Important formula for Mean Time Failures
MTTF - Mean Time To Failure
MTTF = 1 / failure rate = Total #hours of operation / Total #units
MTTF is an averaged value. In reality failure rate changes over time
because it may depend on age of component.
Failure rate = 1 / MTTF (assuming average value over time)
MTTR - Mean Time to Recovery / Repair
MTTR = Total #hours for maintenance / Total #repairs
MTTD - Mean Time to Diagnose
MTBF - Mean Time Between Failures
MTBF = MTTD + MTTR + MTTF
MTTF - Serial assembly
MTTF of system = 1 / SUM (1/MTTFi) for all components i
Failure rate of system = SUM(1/MTTFi) for all components i
MTTF - Parallel assembly
MTTF of system = SUM(MTTFi) for all components i
Availability = Time system is UP and accessible / Total time observed
Availability = MTTF / (MTTD* + MTTR + MTTF)
or
Availability = MTTF / MTBF
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Wednesday, January 4, 2023
Cache performance and Access time of memories
• Cache hit
✓ When CPU refers to memory and find the data or instruction within the Cache Memory
• Cache miss
✓ If the desired data or instruction is not found in the cache memory and CPU refers to the
main memory to find that data or instruction
Hit + Miss = Total CPU ReferenceHit Ratio h = Hit / ( Hit + Miss )
Average access time of any memory system consists of two levels:
✓ Cache Memory
✓ Main Memory
• If Tc is time to access cache memory and Tm is the time to access main
memory and h is the cache hit ration, then
Tavg = Average time to access memoryTavg = h * Tc + ( 1-h ) * ( Tm + Tc )
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Big Data Architecture Challenges
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Apache Technology Ecosystem
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Big Data architecture style
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Tf-IDf in information Retrieval
The tf-idf (term frequency-inverse document frequency) is a measure of the importance of a word in a document or a collection of documents. It is commonly used in information retrieval and natural language processing tasks.
The formula for calculating tf-idf is:
tf-idf = tf * idf
where:
tf (term frequency) is the frequency of the word in the document. It can be calculated as the number of times the word appears in the document divided by the total number of words in the document.
idf (inverse document frequency) is a measure of the rarity of the word. It can be calculated as the logarithm of the total number of documents divided by the number of documents that contain the word.
The resulting tf-idf score for a word reflects both the importance of the word in the specific document and its rarity in the collection of documents. Words that are common across all documents will have a lower tf-idf score, while words that are specific to a particular document and rare in the collection will have a higher tf-idf score.
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The tf-idf (term frequency-inverse document frequency) is a measure of the importance of a word in a document or a collection of documents. It is commonly used in information retrieval and natural language processing tasks.
The formula for calculating tf-idf is:
tf-idf = tf * idf
where:
tf (term frequency) is the frequency of the word in the document. It can be calculated as the number of times the word appears in the document divided by the total number of words in the document.
idf (inverse document frequency) is a measure of the rarity of the word. It can be calculated as the logarithm of the total number of documents divided by the number of documents that contain the word.
The resulting tf-idf score for a word reflects both the importance of the word in the specific document and its rarity in the collection of documents. Words that are common across all documents will have a lower tf-idf score, while words that are specific to a particular document and rare in the collection will have a higher tf-idf score.
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Jaccard Coefficient - Information Retrieval
Links : Example
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Minimum Edit Distance - Information Retrieval
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K-Gram Index (Bigram Indexes) - Information Retrieval
K Gram example : Stanford
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Tuesday, January 3, 2023
Permuterm index - Information Retrieval
Link : Stanford Link
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Difference between Word/term/token and type
Word – A delimited string of characters as it appears in the text.
Term – A “normalized” word (case, morphology, spelling etc); an equivalence class of words.
ex: Same word can be present multiple times, need to consider it all times.
Token – An instance of a word or term occurring in a document.
ex: only time we need to consider how many times the word occurs.
Type – The same as a term in most cases: an equivalence class of tokens.
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Issues with Information Retrieval
Issues with Information Retrieval?
Information Retrieval deals with uncertainty and
vagueness in information systems.
Uncertainty: available representation does typically not reflect true semantics/meaning of objects (text, images, video, etc.)
Vagueness: information need of user lacks clarity, is only vague expressed in query, feedback or user actions.
Differs conceptually from database queries!
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Information Retrieval vs Data Retrieval -- Tabular form
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Monday, January 2, 2023
Deep Learning - Mid Semester - Makeup - DSECLZG524
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