Suppose our Accumulo system stores entire documents in the value column using the following data structure to represent the Gettysburg Address and the Declaration of Independence: rowID family qual time value gettysburg speech script Four score and seven years ago ... When in the course of human events declaration document script Write Map and Reduce pseudocode that determines document similarity according to combinations of three important words. Important words are the words that are not in stop word lists. Stop words are words that are common to all documents that do not provide much indication of the topic of the document. Example stop words include the, аnd, to, but, bеcаuse, an, a, .... Assume that you are given the following array of stop words to use: private String[] stopWords = {"the", "and", "to", "but", "because", "an", "a", ...}; %3D For all combinations of three important (non-stop word) words, create an Accumulo output table that clusters documents according to the important word triples. Two sample output rows from the MapReduce algorithm applied to the table above will look like: rowID family qual time value government:liberty:people speech script gettysburg government:liberty:people document script declaration Make sure that the important words used in the rowID are sorted in alphabetical order so you have only one "government:liberty:people" rowID value, not "government:people:liberty", "liberty:government:people", "liberty:people:government", "people:liberty:government", "people:government:liberty". Also, do not worry about multiple occurrences of any word. Even though the word government appears many times in the Declaration of Independence, just create one output for the "government:liberty:people" triple.
Suppose our Accumulo system stores entire documents in the value column using the following data structure to represent the Gettysburg Address and the Declaration of Independence: rowID family qual time value gettysburg speech script Four score and seven years ago ... When in the course of human events declaration document script Write Map and Reduce pseudocode that determines document similarity according to combinations of three important words. Important words are the words that are not in stop word lists. Stop words are words that are common to all documents that do not provide much indication of the topic of the document. Example stop words include the, аnd, to, but, bеcаuse, an, a, .... Assume that you are given the following array of stop words to use: private String[] stopWords = {"the", "and", "to", "but", "because", "an", "a", ...}; %3D For all combinations of three important (non-stop word) words, create an Accumulo output table that clusters documents according to the important word triples. Two sample output rows from the MapReduce algorithm applied to the table above will look like: rowID family qual time value government:liberty:people speech script gettysburg government:liberty:people document script declaration Make sure that the important words used in the rowID are sorted in alphabetical order so you have only one "government:liberty:people" rowID value, not "government:people:liberty", "liberty:government:people", "liberty:people:government", "people:liberty:government", "people:government:liberty". Also, do not worry about multiple occurrences of any word. Even though the word government appears many times in the Declaration of Independence, just create one output for the "government:liberty:people" triple.
Computer Networking: A Top-Down Approach (7th Edition)
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Transcribed Image Text:The image contains sections labeled "a) Map algorithm pseudocode" and "b) Reduce algorithm pseudocode." Both sections suggest pseudocode explanations for the respective algorithms. However, no specific code or further textual details are visible in the image.
### Explanation of the Map and Reduce Concepts:
**Map Algorithm Pseudocode:**
- The "Map" function takes input data and applies a specified operation to each element, producing a set of intermediate key-value pairs.
- Example pseudocode for a map function:
```
function map(data):
for each element in data:
emit(key, value)
```
**Reduce Algorithm Pseudocode:**
- The "Reduce" function processes the key-value pairs generated by the "Map" function by aggregating the values under each unique key.
- Example pseudocode for a reduce function:
```
function reduce(key, values):
initialize result
for each value in values:
result = aggregate(result, value)
emit(key, result)
```
These algorithms are typically used in distributed computing to process large data sets across clusters effectively.
![**Accumulate System for Document Clustering Using Important Words**
**Overview:**
In this exercise, we explore how to store and process entire documents using the Accumulo system. We represent documents, such as the Gettysburg Address and the Declaration of Independence, in a structured format where essential information is categorized into rows and columns. This setup facilitates determining document similarity based on important words by employing Map and Reduce functions.
**Data Structure:**
The data is structured into a table with columns: `rowID`, `family`, `qual`, `time`, and `value`. Here is a sample of how documents are organized:
| **rowID** | **family** | **qual** | **time** | **value** |
|----------------|------------|----------|----------|------------------------------------|
| gettysburg | speech | script | | Four score and seven years ago ... |
| declaration | document | script | | When in the course of human events ... |
**Algorithm Objective:**
The goal is to write pseudocode for Map and Reduce functions to identify document similarity using combinations of three significant words. Important words are defined as those not included in the stop list, which contains commonly used words that provide minimal context about document content.
**Stop Word List:**
```plaintext
private String[] stopWords = {"the", "and", "to", "but", "because", "an", "a", ...};
```
These stop words are excluded from consideration in determining important word clusters.
**Implementation Strategy:**
1. **Filtering:**
- Filter out stop words from the documents to isolate significant words.
2. **Combinations:**
- Generate all possible combinations of three important words (triples).
3. **Map Phase:**
- For each combination, create an output table in the Accumulo database. This table organizes documents based on important word triples, grouping similar documents together.
**Sample Output:**
The MapReduce algorithm processes the data to produce outputs that look like this, clustering documents under combinations of important words:
| **rowID** | **family** | **qual** | **time** | **value** |
|----------------------------|------------|----------|----------|---------------|
| government:liberty:people | speech | script | | gettysburg |
| government:liberty:people | document | script | | declaration](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbfb231a0-55d2-43f5-af17-b58a2c7b4a3a%2Ffed234f4-04ba-45f6-9d5e-04d532214e0b%2Fhmmieje_processed.png&w=3840&q=75)
Transcribed Image Text:**Accumulate System for Document Clustering Using Important Words**
**Overview:**
In this exercise, we explore how to store and process entire documents using the Accumulo system. We represent documents, such as the Gettysburg Address and the Declaration of Independence, in a structured format where essential information is categorized into rows and columns. This setup facilitates determining document similarity based on important words by employing Map and Reduce functions.
**Data Structure:**
The data is structured into a table with columns: `rowID`, `family`, `qual`, `time`, and `value`. Here is a sample of how documents are organized:
| **rowID** | **family** | **qual** | **time** | **value** |
|----------------|------------|----------|----------|------------------------------------|
| gettysburg | speech | script | | Four score and seven years ago ... |
| declaration | document | script | | When in the course of human events ... |
**Algorithm Objective:**
The goal is to write pseudocode for Map and Reduce functions to identify document similarity using combinations of three significant words. Important words are defined as those not included in the stop list, which contains commonly used words that provide minimal context about document content.
**Stop Word List:**
```plaintext
private String[] stopWords = {"the", "and", "to", "but", "because", "an", "a", ...};
```
These stop words are excluded from consideration in determining important word clusters.
**Implementation Strategy:**
1. **Filtering:**
- Filter out stop words from the documents to isolate significant words.
2. **Combinations:**
- Generate all possible combinations of three important words (triples).
3. **Map Phase:**
- For each combination, create an output table in the Accumulo database. This table organizes documents based on important word triples, grouping similar documents together.
**Sample Output:**
The MapReduce algorithm processes the data to produce outputs that look like this, clustering documents under combinations of important words:
| **rowID** | **family** | **qual** | **time** | **value** |
|----------------------------|------------|----------|----------|---------------|
| government:liberty:people | speech | script | | gettysburg |
| government:liberty:people | document | script | | declaration
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