This document provides an overview of evaluation measures for information retrieval systems. It discusses why evaluation is important for improving systems and measuring user satisfaction. Key points include:
- Common set-based measures include recall, precision, and F-measure. Ranked retrieval measures include average precision (AP), normalized discounted cumulative gain (nDCG), expected reciprocal rank (ERR), and Q-measure for graded relevance.
- Measures for diversified search aim to balance relevance and diversity across different user intents. Examples given include α-nDCG, ERR-IA, D#-nDCG, and U-IA.
- Statistical significance testing allows determining whether differences between systems are likely real or due to chance. The t
Recommendation Engine Powered by HadoopPranab Ghosh
This document summarizes a presentation about building a recommendation engine powered by Hadoop. It discusses how Hadoop allows for parallel processing of large datasets using a functional programming model. It then describes how collaborative filtering and model-based recommendation algorithms can be implemented on Hadoop through MapReduce jobs. Specifically, it outlines two MapReduce jobs to calculate item correlations and predict user ratings for collaborative filtering. The predicted ratings can then be used to provide recommendations.
This document discusses ranking query results in databases to return the most relevant results. It addresses two common problems: empty answers, when a query returns no results, and many answers, when a query returns too many results. For empty answers, it proposes automated ranking functions to return approximately matching tuples without revising the query. For many answers, it adapts probabilistic information retrieval models to rank tuples based on global and conditional scores of specified and unspecified attributes. The document also describes implementing a ranking system with pre-processing, intermediate storage, and a query processing component.
Made to Measure: Ranking Evaluation using ElasticsearchDaniel Schneiter
The document describes ranking evaluation in Elasticsearch using its rank_eval API. Ranking evaluation allows measuring search quality through repeatable testing across different user needs. It defines typical search queries and rates documents to calculate metrics like precision, reciprocal rank, and discounted cumulative gain. This provides a way to quickly iterate on search and optimize for more user needs compared to methods like A/B testing. The demo uses Wikipedia data to illustrate the ranking evaluation process and API.
Predicting Multiple Metrics for Queries: Better Decision Enabled by Machine L...Soheila Dehghanzadeh
This document proposes using machine learning to simultaneously predict multiple performance metrics (running time, resource usage, etc.) for queries prior to execution. It describes building models based on training data from past query executions that map query features to performance features. Specifically, it uses KCCA to find dimensions of maximal correlation between query and performance features to define similarity. The models predict by weighting the performance metrics of similar past queries. Experiments show the approach can accurately predict time across different query types and databases.
The document describes a technique called STRICT that uses TextRank and POSRank algorithms to identify important terms from a software change task description to generate an effective initial search query. An experiment on 1,939 change tasks from 8 open source projects found that STRICT improved the query effectiveness in 57.84% of cases compared to baseline queries like title alone. STRICT also showed better retrieval performance based on metrics like mean average precision and mean recall compared to state-of-the-art techniques. The approach validates the use of graph-based ranking algorithms to address the challenge of generating relevant initial search queries from natural language change task descriptions.
1. The document presents a project to develop a browser plugin that visually represents knowledge articles from sources like Wikipedia as connected graph nodes. This helps users easily understand topics by showing the main keywords and their relationships.
2. The project aims to implement an interactive e-learning tool that displays a graphical view of document content as nodes linked by their semantic relationships.
3. The algorithm extracts keywords from articles and calculates weights based on frequency to determine the most prominent nodes and links to display at different depths of the graph.
This document provides an overview of evaluation measures for information retrieval systems. It discusses why evaluation is important for improving systems and measuring user satisfaction. Key points include:
- Common set-based measures include recall, precision, and F-measure. Ranked retrieval measures include average precision (AP), normalized discounted cumulative gain (nDCG), expected reciprocal rank (ERR), and Q-measure for graded relevance.
- Measures for diversified search aim to balance relevance and diversity across different user intents. Examples given include α-nDCG, ERR-IA, D#-nDCG, and U-IA.
- Statistical significance testing allows determining whether differences between systems are likely real or due to chance. The t
Recommendation Engine Powered by HadoopPranab Ghosh
This document summarizes a presentation about building a recommendation engine powered by Hadoop. It discusses how Hadoop allows for parallel processing of large datasets using a functional programming model. It then describes how collaborative filtering and model-based recommendation algorithms can be implemented on Hadoop through MapReduce jobs. Specifically, it outlines two MapReduce jobs to calculate item correlations and predict user ratings for collaborative filtering. The predicted ratings can then be used to provide recommendations.
This document discusses ranking query results in databases to return the most relevant results. It addresses two common problems: empty answers, when a query returns no results, and many answers, when a query returns too many results. For empty answers, it proposes automated ranking functions to return approximately matching tuples without revising the query. For many answers, it adapts probabilistic information retrieval models to rank tuples based on global and conditional scores of specified and unspecified attributes. The document also describes implementing a ranking system with pre-processing, intermediate storage, and a query processing component.
Made to Measure: Ranking Evaluation using ElasticsearchDaniel Schneiter
The document describes ranking evaluation in Elasticsearch using its rank_eval API. Ranking evaluation allows measuring search quality through repeatable testing across different user needs. It defines typical search queries and rates documents to calculate metrics like precision, reciprocal rank, and discounted cumulative gain. This provides a way to quickly iterate on search and optimize for more user needs compared to methods like A/B testing. The demo uses Wikipedia data to illustrate the ranking evaluation process and API.
Predicting Multiple Metrics for Queries: Better Decision Enabled by Machine L...Soheila Dehghanzadeh
This document proposes using machine learning to simultaneously predict multiple performance metrics (running time, resource usage, etc.) for queries prior to execution. It describes building models based on training data from past query executions that map query features to performance features. Specifically, it uses KCCA to find dimensions of maximal correlation between query and performance features to define similarity. The models predict by weighting the performance metrics of similar past queries. Experiments show the approach can accurately predict time across different query types and databases.
The document describes a technique called STRICT that uses TextRank and POSRank algorithms to identify important terms from a software change task description to generate an effective initial search query. An experiment on 1,939 change tasks from 8 open source projects found that STRICT improved the query effectiveness in 57.84% of cases compared to baseline queries like title alone. STRICT also showed better retrieval performance based on metrics like mean average precision and mean recall compared to state-of-the-art techniques. The approach validates the use of graph-based ranking algorithms to address the challenge of generating relevant initial search queries from natural language change task descriptions.
1. The document presents a project to develop a browser plugin that visually represents knowledge articles from sources like Wikipedia as connected graph nodes. This helps users easily understand topics by showing the main keywords and their relationships.
2. The project aims to implement an interactive e-learning tool that displays a graphical view of document content as nodes linked by their semantic relationships.
3. The algorithm extracts keywords from articles and calculates weights based on frequency to determine the most prominent nodes and links to display at different depths of the graph.
Every team working on information retrieval software struggles with the task of evaluating how well their system performs in terms of search quality(currently and historically). Evaluating search quality is important both to understand and size the improvement or regression of your search application across the development cycles, and to communicate such progress to relevant stakeholders. In the industry, and especially in the open source community, the landscape is quite fragmented: such requirements are often achieved using ad-hoc partial solutions that each time require a considerable amount of development and customization effort. To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend.
Optimized Access Strategies for a Distributed Database DesignWaqas Tariq
Abstract Distributed Database Query Optimization has been an active area of research for Database research Community in this decade. Research work mostly involves mathematical programming and evolving new algorithm design techniques in order to minimize the combined cost of storing the database, processing transactions and communication amongst various sites of storage. The complete problem and most of its subsets as well are NP-Hard. Most of proposed solutions till date are based on use of Enumerative Techniques or using Heuristics. In this paper we have shown benefits of using innovative Genetic Algorithms (GA) for optimizing the sequence of sub-query operations over the enumerative methods and heuristics. A stochastic simulator has been designed and experimental results show encouraging improvements in decreasing the total cost of a query. An exhaustive enumerative method is also applied and solutions are compared with that of GA on various parameters of a Distributed Query, like up to 12 joins and 10 sites. Keywords: Distributed Query Optimization, Database Statistics, Query Execution Plan, Genetic Algorithms, Operation Allocation.
Mining Query Log to Suggest Competitive Keyphrases for Sponsored Search Via I...IRJET Journal
The document describes a proposed method called ITCK (Improved Topic based Competitive Keyphrase) for suggesting relevant keywords for sponsored search advertising. It involves two main steps:
1) Generating candidate keywords for a seed keyword by analyzing associations between keywords in a search query log using association rule mining.
2) Applying an improved topic modeling method based on latent Dirichlet allocation (LDA) to the candidate keywords to suggest competitive keywords grouped by topic. The proposed ITCK method is evaluated using an AOL search query log and is shown to perform better than existing keyword suggestion methods.
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachAlessandro Benedetti
Every information retrieval practitioner ordinarily struggles with the task of evaluating how well a search engine is performing and to reproduce the performance achieved in a specific point in time.
Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
Additionally it is extremely important to track the evolution of the search system in time and to be able to reproduce and measure the same performance (through metrics of interest such as precison@k, recall, NDCG@k...).
The talk will describe the Rated Ranking Evaluator from a researcher and software engineer perspective.
RRE is an open source search quality evaluation tool, that can be used to produce a set of reports about the quality of a system, iteration after iteration and that could be integrated within a continuous integration infrastructure to monitor quality metrics after each release .
Focus of the talk will be to raise public awareness of the topic of search quality evaluation and reproducibility describing how RRE could help the industry.
This document summarizes the analysis of a cloud workload using Google trace data. The key steps included:
1) Preprocessing and analyzing the Google trace data to identify important attributes like CPU and memory usage.
2) Calculating resource usage statistics and classification of users into clusters based on estimation ratios using clustering algorithms. Target users who overestimated resources were identified.
3) Performing time series analysis using DTW on tasks of target users to identify patterns and cluster tasks with similar workloads.
Review Mining of Products of Amazon.comShobhit Monga
The document outlines the phases of a major project involving analyzing product reviews. Phase 1 involved selecting a problem statement and collecting review data. Phase 2 involved preprocessing the data by removing stop words and stemming words. Features were then extracted. Phase 3 involved implementing k-means clustering on the normalized feature set to group similar reviews and analyze the results. Porter stemming and k-means clustering are also briefly defined.
Exploring Correlation Between Sentiment of Environmental Tweets and the Stock...Data Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Fortune 500 Company Performance Analysis Using Social Networks
Speaker: Yi-Shan Shir
This presentation focus on studying the correlation between financial performance and social media relationship and behavior of Fortune 500 companies. The findings from this research can assist in the prediction of Fortune 500 stock performance based on a number of social network analysis metrics.
IRJET- Automatic Text Summarization using Text RankIRJET Journal
This document summarizes a research paper that proposes an automatic text summarization system using the Text Rank algorithm. The system takes in data from multiple sources on a particular topic and generates a concise summary bullet points without requiring the user to visit each individual site. It first concatenates and pre-processes the text from various articles, represents each sentence as a vector, calculates similarity between sentences to create a graph, then ranks sentences using PageRank to extract the top sentences for the summary. The proposed system aims to make knowledge gathering easier by providing summarized overviews of technical topics rather than requiring users to read multiple lengthy articles.
The 2008 parliamentary elections in Mongolia resulted in post-election demonstrations and violence. Key issues included an unconstitutional electoral law that favored large parties, problems with voter registration and lists, and decisions by the General Election Commission that violated the electoral law and damaged public trust in the process. Irregularities in the voting process, such as people voting without proper identification, fueled protests over the fairness and legitimacy of the election results.
The document discusses water deficit, which refers to a situation where available water within a region is less than demand. It notes that while most developed countries supply drinking water, only a small portion is actually consumed, with the rest used for washing and landscaping. Nearly 1 billion people worldwide do not have access to safe drinking water, which can lead to diseases from pathogens or toxins if used for drinking or food preparation. Solutions proposed include improved access to clean water sources.
1-800-PetMeds September 2011 Business PlanPet Meds
The document contains financial data for an unnamed company from fiscal years 2006 to 2011. It shows steady increases in total net revenue each year, growing from $162.2 million in FY2006 to $231.6 million in FY2011. Net income also increased each year over the period shown, rising from $12.1 million in FY2006 to $20.9 million in FY2011. Additional charts provide details on revenue sources, expenses, and other financial metrics for the company over multiple quarters and fiscal years.
The document discusses water deficit, which refers to a situation where available water within a region is less than demand. It notes that while most developed countries supply drinking water, only a small portion is actually consumed, with the rest used for washing and landscaping. Nearly 1 billion people worldwide do not have access to safe drinking water, which can lead to diseases from pathogens or toxins if used for drinking or food preparation. Solutions and further resources on the topic are provided.
The document discusses stream connectors in software systems. A stream connector provides communication services to transfer large amounts of data between autonomous processes. Examples of stream connectors include Unix pipes, TCP/UDP sockets, and proprietary client-server protocols. The example code provided shows how to connect to a game server using sockets and streams to send a JOIN message. Streams can vary based on factors like communication cardinality, data format, synchronicity, locality, identity, state, throughput, buffering, bounds, and delivery effort.
The document discusses various corporate laws that are important for corporate citizens to understand, including the Companies Act, Contract Act, Negotiable Instruments Act, and intellectual property laws. It defines a company and outlines the characteristics of public and private companies. It also describes the formation process for a company, contents of the memorandum and articles of association, and key concepts like prospectus, kinds of companies, and corporate veil.
The document summarizes the team's approach to the KDD Cup 2014 competition to predict exciting projects on the DonorsChoose.org platform. It describes the provided data, data preprocessing steps including handling missing data and feature encoding. It then discusses the main methods used: random forests, gradient boosting regression trees, and logistic regression. The team also tested neural networks but faced challenges training the models. Their final submission was an ensemble of the three main methods, weighted based on their performance.
Summary_Classification_Algorithms_Student_DataMadeleine Organ
This project aimed to classify students into achievement levels based on school and home factors using classification algorithms. The authors implemented KNN and NB algorithms on a dataset of 395 students with 10 attributes. They tested different parameter configurations and achieved error rates ranging from 9-22%. They developed an interactive program that allows a user to input data for a new student and predict their class. Potential extensions include using additional attributes, algorithms, and allowing more user customization of algorithm configurations and attributes.
CGPA otherwise called Cumulative Grade Points. Average is the normal of Grade Points acquired in every one of the subjects secured till date. It is trusted that it gives a general knowledge into the level of devotion, truthfulness and diligent work put by the understudy.
However there might be where an understudy who is remarkable at programming may not appreciate other hypothetical subjects like programming testing. Notwithstanding, CGPA comes up short when such a situation comes into picture.
This document describes a project analyzing bike sharing demand data using the SEMMA process. It explores and modifies the data, builds multiple regression, decision tree, boosted tree, bootstrap forest, and neural network models, and assesses the models. Key steps included adding new variables, removing outliers, splitting the data into training, validation, and test sets, and modeling with different algorithms to predict bike rent count and compare model performance. The best predictors of demand were found to be time of day, day of week, season, temperature, and humidity.
This document analyzes a cloud workload dataset from Google to characterize usage patterns. The key steps are:
1) The data is preprocessed and important attributes like CPU/memory usage are analyzed.
2) Clustering algorithms are used to classify users based on resource estimation ratios and tasks based on attributes.
3) Time series analysis via DTW is performed on tasks to identify patterns, and tasks are clustered.
4) For target high estimation ratio users, resource usage is predicted based on matching task patterns and allocated dynamically with a threshold to allow for spikes. This approach aims to reallocate unused resources to other users.
This document provides details of a student project to build a stock market predictor. It includes:
- Names of 4 students working on the project and their guide
- Sections on literature review of papers on stock prediction methods like regression, logistic regression and SVM classification
- A Gantt chart showing the project timeline and tasks from October to April
- Details of the methodology including loading a pre-trained model, making predictions, and displaying results
- Explanations of the modelling process including data collection, preprocessing, training a LSSVR model, and predicting stock values
- A project plan outlining members' roles and responsibilities in planning, data collection, training and testing the model
-
Every team working on information retrieval software struggles with the task of evaluating how well their system performs in terms of search quality(currently and historically). Evaluating search quality is important both to understand and size the improvement or regression of your search application across the development cycles, and to communicate such progress to relevant stakeholders. In the industry, and especially in the open source community, the landscape is quite fragmented: such requirements are often achieved using ad-hoc partial solutions that each time require a considerable amount of development and customization effort. To provide a standard, unified and approachable technology, we developed the Rated Ranking Evaluator (RRE), an open source tool for evaluating and measuring the search quality of a given search infrastructure. RRE is modular, compatible with multiple search technologies and easy to extend.
Optimized Access Strategies for a Distributed Database DesignWaqas Tariq
Abstract Distributed Database Query Optimization has been an active area of research for Database research Community in this decade. Research work mostly involves mathematical programming and evolving new algorithm design techniques in order to minimize the combined cost of storing the database, processing transactions and communication amongst various sites of storage. The complete problem and most of its subsets as well are NP-Hard. Most of proposed solutions till date are based on use of Enumerative Techniques or using Heuristics. In this paper we have shown benefits of using innovative Genetic Algorithms (GA) for optimizing the sequence of sub-query operations over the enumerative methods and heuristics. A stochastic simulator has been designed and experimental results show encouraging improvements in decreasing the total cost of a query. An exhaustive enumerative method is also applied and solutions are compared with that of GA on various parameters of a Distributed Query, like up to 12 joins and 10 sites. Keywords: Distributed Query Optimization, Database Statistics, Query Execution Plan, Genetic Algorithms, Operation Allocation.
Mining Query Log to Suggest Competitive Keyphrases for Sponsored Search Via I...IRJET Journal
The document describes a proposed method called ITCK (Improved Topic based Competitive Keyphrase) for suggesting relevant keywords for sponsored search advertising. It involves two main steps:
1) Generating candidate keywords for a seed keyword by analyzing associations between keywords in a search query log using association rule mining.
2) Applying an improved topic modeling method based on latent Dirichlet allocation (LDA) to the candidate keywords to suggest competitive keywords grouped by topic. The proposed ITCK method is evaluated using an AOL search query log and is shown to perform better than existing keyword suggestion methods.
Search Quality Evaluation to Help Reproducibility: An Open-source ApproachAlessandro Benedetti
Every information retrieval practitioner ordinarily struggles with the task of evaluating how well a search engine is performing and to reproduce the performance achieved in a specific point in time.
Improving the correctness and effectiveness of a search system requires a set of tools which help measuring the direction where the system is going.
Additionally it is extremely important to track the evolution of the search system in time and to be able to reproduce and measure the same performance (through metrics of interest such as precison@k, recall, NDCG@k...).
The talk will describe the Rated Ranking Evaluator from a researcher and software engineer perspective.
RRE is an open source search quality evaluation tool, that can be used to produce a set of reports about the quality of a system, iteration after iteration and that could be integrated within a continuous integration infrastructure to monitor quality metrics after each release .
Focus of the talk will be to raise public awareness of the topic of search quality evaluation and reproducibility describing how RRE could help the industry.
This document summarizes the analysis of a cloud workload using Google trace data. The key steps included:
1) Preprocessing and analyzing the Google trace data to identify important attributes like CPU and memory usage.
2) Calculating resource usage statistics and classification of users into clusters based on estimation ratios using clustering algorithms. Target users who overestimated resources were identified.
3) Performing time series analysis using DTW on tasks of target users to identify patterns and cluster tasks with similar workloads.
Review Mining of Products of Amazon.comShobhit Monga
The document outlines the phases of a major project involving analyzing product reviews. Phase 1 involved selecting a problem statement and collecting review data. Phase 2 involved preprocessing the data by removing stop words and stemming words. Features were then extracted. Phase 3 involved implementing k-means clustering on the normalized feature set to group similar reviews and analyze the results. Porter stemming and k-means clustering are also briefly defined.
Exploring Correlation Between Sentiment of Environmental Tweets and the Stock...Data Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Fortune 500 Company Performance Analysis Using Social Networks
Speaker: Yi-Shan Shir
This presentation focus on studying the correlation between financial performance and social media relationship and behavior of Fortune 500 companies. The findings from this research can assist in the prediction of Fortune 500 stock performance based on a number of social network analysis metrics.
IRJET- Automatic Text Summarization using Text RankIRJET Journal
This document summarizes a research paper that proposes an automatic text summarization system using the Text Rank algorithm. The system takes in data from multiple sources on a particular topic and generates a concise summary bullet points without requiring the user to visit each individual site. It first concatenates and pre-processes the text from various articles, represents each sentence as a vector, calculates similarity between sentences to create a graph, then ranks sentences using PageRank to extract the top sentences for the summary. The proposed system aims to make knowledge gathering easier by providing summarized overviews of technical topics rather than requiring users to read multiple lengthy articles.
The 2008 parliamentary elections in Mongolia resulted in post-election demonstrations and violence. Key issues included an unconstitutional electoral law that favored large parties, problems with voter registration and lists, and decisions by the General Election Commission that violated the electoral law and damaged public trust in the process. Irregularities in the voting process, such as people voting without proper identification, fueled protests over the fairness and legitimacy of the election results.
The document discusses water deficit, which refers to a situation where available water within a region is less than demand. It notes that while most developed countries supply drinking water, only a small portion is actually consumed, with the rest used for washing and landscaping. Nearly 1 billion people worldwide do not have access to safe drinking water, which can lead to diseases from pathogens or toxins if used for drinking or food preparation. Solutions proposed include improved access to clean water sources.
1-800-PetMeds September 2011 Business PlanPet Meds
The document contains financial data for an unnamed company from fiscal years 2006 to 2011. It shows steady increases in total net revenue each year, growing from $162.2 million in FY2006 to $231.6 million in FY2011. Net income also increased each year over the period shown, rising from $12.1 million in FY2006 to $20.9 million in FY2011. Additional charts provide details on revenue sources, expenses, and other financial metrics for the company over multiple quarters and fiscal years.
The document discusses water deficit, which refers to a situation where available water within a region is less than demand. It notes that while most developed countries supply drinking water, only a small portion is actually consumed, with the rest used for washing and landscaping. Nearly 1 billion people worldwide do not have access to safe drinking water, which can lead to diseases from pathogens or toxins if used for drinking or food preparation. Solutions and further resources on the topic are provided.
The document discusses stream connectors in software systems. A stream connector provides communication services to transfer large amounts of data between autonomous processes. Examples of stream connectors include Unix pipes, TCP/UDP sockets, and proprietary client-server protocols. The example code provided shows how to connect to a game server using sockets and streams to send a JOIN message. Streams can vary based on factors like communication cardinality, data format, synchronicity, locality, identity, state, throughput, buffering, bounds, and delivery effort.
The document discusses various corporate laws that are important for corporate citizens to understand, including the Companies Act, Contract Act, Negotiable Instruments Act, and intellectual property laws. It defines a company and outlines the characteristics of public and private companies. It also describes the formation process for a company, contents of the memorandum and articles of association, and key concepts like prospectus, kinds of companies, and corporate veil.
The document summarizes the team's approach to the KDD Cup 2014 competition to predict exciting projects on the DonorsChoose.org platform. It describes the provided data, data preprocessing steps including handling missing data and feature encoding. It then discusses the main methods used: random forests, gradient boosting regression trees, and logistic regression. The team also tested neural networks but faced challenges training the models. Their final submission was an ensemble of the three main methods, weighted based on their performance.
Summary_Classification_Algorithms_Student_DataMadeleine Organ
This project aimed to classify students into achievement levels based on school and home factors using classification algorithms. The authors implemented KNN and NB algorithms on a dataset of 395 students with 10 attributes. They tested different parameter configurations and achieved error rates ranging from 9-22%. They developed an interactive program that allows a user to input data for a new student and predict their class. Potential extensions include using additional attributes, algorithms, and allowing more user customization of algorithm configurations and attributes.
CGPA otherwise called Cumulative Grade Points. Average is the normal of Grade Points acquired in every one of the subjects secured till date. It is trusted that it gives a general knowledge into the level of devotion, truthfulness and diligent work put by the understudy.
However there might be where an understudy who is remarkable at programming may not appreciate other hypothetical subjects like programming testing. Notwithstanding, CGPA comes up short when such a situation comes into picture.
This document describes a project analyzing bike sharing demand data using the SEMMA process. It explores and modifies the data, builds multiple regression, decision tree, boosted tree, bootstrap forest, and neural network models, and assesses the models. Key steps included adding new variables, removing outliers, splitting the data into training, validation, and test sets, and modeling with different algorithms to predict bike rent count and compare model performance. The best predictors of demand were found to be time of day, day of week, season, temperature, and humidity.
This document analyzes a cloud workload dataset from Google to characterize usage patterns. The key steps are:
1) The data is preprocessed and important attributes like CPU/memory usage are analyzed.
2) Clustering algorithms are used to classify users based on resource estimation ratios and tasks based on attributes.
3) Time series analysis via DTW is performed on tasks to identify patterns, and tasks are clustered.
4) For target high estimation ratio users, resource usage is predicted based on matching task patterns and allocated dynamically with a threshold to allow for spikes. This approach aims to reallocate unused resources to other users.
This document provides details of a student project to build a stock market predictor. It includes:
- Names of 4 students working on the project and their guide
- Sections on literature review of papers on stock prediction methods like regression, logistic regression and SVM classification
- A Gantt chart showing the project timeline and tasks from October to April
- Details of the methodology including loading a pre-trained model, making predictions, and displaying results
- Explanations of the modelling process including data collection, preprocessing, training a LSSVR model, and predicting stock values
- A project plan outlining members' roles and responsibilities in planning, data collection, training and testing the model
-
1. Software project estimation involves decomposing a project into smaller problems like major functions and activities. Estimates can be based on similar past projects, decomposition techniques, or empirical models.
2. Accurate estimates depend on properly estimating the size of the software product using techniques like lines of code, function points, or standard components. Baseline metrics from past projects are then applied to the size estimates.
3. Decomposition techniques involve estimating the effort needed for each task or function and combining them. Process-based estimation decomposes the software process into tasks while problem-based estimation decomposes the problem.
This document is a research proposal on attribute selection and representation for software defect prediction. The proposal discusses limitations in existing attribute selection methods and the importance of pre-processing data. It aims to propose a new attribute selection method that improves accuracy by addressing shortcomings, and to study appropriate classifiers. The methodology involves a literature review on pre-processing, attribute selection and classification methods. It will then propose and implement a new attribute selection process, compare it using different classifiers and pre-processing, and evaluate it against existing techniques in a technical report.
The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
The document provides a project implementation report analyzing Net Promoter Score (NPS) for Hyatt Hotel Group. Key analyses included identifying patterns in customer visits on Valentine's Day across the US, determining which guest demographics are most likely to be promoters, and correlations between nightly rates, length of stay, purpose of visit, and customer satisfaction. Recommendations include offering Valentine's packages, discounts for older and younger guests, and adjusting nightly rates based on length of stay patterns in different countries. The report covered results from a variety of statistical methods to analyze the hotel customer data and provide actionable insights.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes different indexing models for multiple queries called Indexing for Multiple Queries (IMQ). It first proposes using k-Nearest Neighbor (kNN) indexing for queries. It then describes an online method that creates an indexing model for each query based on similar labeled queries. It also describes two offline methods that pre-create indexing models to improve efficiency. Experimental results showed the online and offline kNN methods performed better than a baseline single indexing model method.
CompSci: 221 Winter 2017 Search Engine for UCISoham Kulkarni
▪ Implemented text processing for web pages to extract tokens, n- grams and anagrams in Python
▪ Designed a spider to crawl ics.uci.edu domain and accumulate crawled pages into a MySQL database
▪ Constructed an indexer for crawled pages and a page ranking mechanism based on Page Rank and collection frequency
Attribute Reduction:An Implementation of Heuristic Algorithm using Apache SparkIRJET Journal
This document discusses an attribute reduction algorithm for weather forecast data using Apache Spark. It aims to reduce redundant attributes in weather forecast data to improve data mining algorithm performance and reduce costs. The algorithm uses rough set theory to build a weather forecast knowledge representation system. It proposes using Spark's in-memory computing benefits to implement an attribute reduction algorithm that uses a heuristic formula to reduce the search space and simplify the weather forecast decision table, improving computation power. The algorithm is tested on a small cluster to evaluate its ability to efficiently perform attribute reduction in parallel.
This document proposes a new approach for software project estimation that combines existing estimation techniques. It involves using case-based reasoning to retrieve similar past projects, reusing their estimates, and revising the estimates based on new parameters and delay-causing incidents. The approach allows parameters to be added dynamically during project execution to make estimates more context-sensitive and help converge to actual values. A prototype tool has been implemented to demonstrate calculating estimates by dynamically selecting parameters and computing similarity indexes between current and past projects.
What is Rated Ranking Evaluator and how to use it (for both Software Engineer and IT Manager). Talk made during Chorus Workshops at Plainschwarz Salon.
Automated Essay Grading using Features SelectionIRJET Journal
This document describes research on developing an automated essay grading system using feature selection. The researchers extracted various features from essays, including bag-of-words, word and sentence counts, and structure. They used a sequential forward feature selection algorithm to identify the optimal subset of features that achieved the highest grading accuracy. This algorithm starts with an empty feature set and sequentially adds individual features, retaining features that improve the accuracy at each step. The goal is to develop a system that can grade essays consistently and reduce the labor required from human graders.
Final Defence.pptxFinal Defence.pptxFinal Defence.pptxFinal Defence.pptxFinal...Chaudhry Hussain
This document outlines the presentation for a master's seminar on generalized linear models with gradient descent optimization. The presentation includes an introduction, literature survey, problem statement, aims and objectives, methodology, tools used, results, conclusion, and references. It discusses external review feedback incorporated into the work, including narrowing the title, adding preprocessing comments, comparing model performance, and highlighting contributions. The methodology describes data cleaning, feature engineering, outlier detection/removal, one-hot encoding, model creation, and gradient descent approach. Results show the linear regression model achieving the best accuracy of 86%.
Data Science Introduction: Concepts, lifecycle, applications.pptxsumitkumar600840
This document provides an introduction to the subject of data visualization using R programming and Power BI. It discusses key concepts in data science including the data science lifecycle, components of data science like statistics and machine learning, and applications of data science such as image recognition. The document also outlines some advantages and disadvantages of using data science.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
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1. CS4642 - Data Mining & Information
Retrieval
Paper Based on KDDCup 2014 Submission
Group Members:
100227D - Jayaweera W.J.A.I.U.
100470N - Sajeewa G.K.M.C
100476M - Sampath P.L.B.
100612E - Wijewardane M.M.D.T.K.
Group Number : 13
Final Group Rank : 76
2. Description of Data
In this competition, five data files are available for competitors. They are donations
(contains information about the donations to each project. This is only provided for projects
in the training set), essays (contains project text posted by the teachers. This is provided for
both the training and test set), projects (contains information about each project. This is
provided for both the training and test set), resources (contains information about the
resources requested for each project. This is provided for both the training and test set) and
outcomes (contains information about the outcomes of projects in the training set). Before
starting the knowledge discovery process provided data have been analyzed.
First of all number of data records in each file has been counted to get an idea about the
amount of data available. Projects file has 664098 records, essays file has 664098 records,
outcomes file has 619326 records, resources file has 3667217 records and donations file has
3097989. Our next task was to identify the criterion which is used to differentiate test data
from training data. After reading the competition details we realized that projects after
2014-01-01 belongs to test data set and projects before 2014-01-01 belongs to training data
set. According to that 619326 projects are available for training set and remaining amount
(44772) of projects are for training set. For each of the project in training set, project
description, essay data, resources requested, donations provided and outcome are given. For
each of the project in test set, project description, essay data, resources requested are given.
Data Imbalanced Problem
After having a brief understanding of data provided we started to analyze training set.
When we draw a graph between the project’s posted dates and “is_exciting” attribute, we
realized that there are no exciting projects before April 2014. Graph was completely
skewed to the right side.
This leads to a data imbalanced problem as number of exciting projects is very small
compare to the number of non-exciting projects (exciting - 5.9274%). Histogram of
exciting and non-exciting projects was as follows.
3. In competition forum there was an explanation for this problem. It said that organization
might not keep track of some of the requirements needed to decide ‘is_exciting’ for the
projects before 2010. Therefore we thought that classification given in outcomes file before
2010 may not correct and we decided to use down sampling technique to handle
imbalanced data (remove projects before 2010). It is true that valuable information may get
lost when projects are removed. But accuracy obtained by removing that data outweigh the
loss of information. Therefore we were able to obtain higher accuracy by down sampling
the given data. All the classifiers that we have used performed well after removing projects
before 2010.
Preprocessing Data
First we analyzed characteristics of mining data using statistical measurements. Using the
data frame describe method we calculated number of records, mean, standard deviation,
minimum value, maximum value and the quartile values for each attribute. Given below is
a statistical measurement of two attributes.
We were able to get an idea about the distribution of attributes using these statistical
measurements.
4. Filling Missing Values
Initially we used pad method (propagate last valid observation forward) to fill missing
values of all the attributes. But we realized that we can achieve high accuracy by selecting a
filling method based on the type of the attribute. To do that first we calculate the percentage
of missing values. It was as follows,
Highest amount of missing values percentage was for secondary focused subject and
secondary focused area. This is because some projects may have only primary focus area
and primary focus subject. We decided to fill missing secondary values with their
respective primary values. Also we used linear interpolation for numeric values and for
other attributes we used pad method. Later when we tune up classifiers we changed the
method from pad to backfill (use next valid observation) as it obtained a higher accuracy
than pad.
Remove Outliers
When we analyzed data, outliers were detected in some of the attributes. We used scatter
plots to identify outliers. There were outliers in cost related attributes and we replaced them
with the mean value of that attribute. Given below is outlier analysis of cost attribute,
5. Red circle value can be considered as an outlier as it has a really huge value than other
values. These outliers have caused a lot of problems when we discretize data. To identify
outliers in resources, we used inter quartile range as a measurement.
Label Encoding
We did not use all the attributes for predictions. We focused more on repetitive features as
they will help more to the classifier to make predictions. Most of these repetitive
features/attributes have string values rather than numerical values. Available classifiers do
not accept string values for features. So we used label encoder to transform those string
values to integer values between 0 and n-1, n being the number of different values a feature
can take.
But classifiers expect continuous input and may interpret the categories as being ordered
which is not desired. To make the categorical features to features that can be used with
scikit classifiers we used one-hot encoding. Encoder transformed each categorical feature
with k possible values into k binary features, with only one active for particular sample.
This improved the performance of classifiers to greater extent. For an example SGD
classifier obtained about 0.55 ROC score without hot encoding and with encoding it
obtained about 0.59 ROC score.
Continues Values Discretization
Project attributes such as school longitude, school latitude, zip code and total cost cannot be
directly used for predictions as they are less likely to be repetitive. But this information
cannot be eliminated as they may help to get decisions for classifiers. To make these
attributes more repetitive we used discretization. We put these continuous values into bins
and used the bin index as the attribute. For an example we used discretization for longitude
and latitude and divided projects into five regions (bins) and used region id instead of using
longitude and latitude. Discretization results for total cost attribute as follows,
6. We applied the same concept for cost related attributes, item count for project, total price of
items per project, number of projects per teacher etc.
This has improved the repetitiveness of attributes to a greater extent and more useful
information has been discovered which can be used by the classifier.
Attribute Construction
Some of the features given in data files cannot be used directly due to various reasons (most
of the times they are highly non repetitive). We used some of these features to construct
new features by combining multiple features or transforming one to another. Given below
is the list of derived attributes.
1. Month- posted date of the project was given but it is less repetitive. We derived
month attribute from the posted date and used it for prediction
2. Essay length- for each project corresponding essay was given but it cannot be used
directly for prediction. Therefore we calculated the length of the each essay after
removing extra spaces within the essay text and used it as an attribute.
3. Need statement length
4. Projects per teacher- we calculated number of projects per teacher by grouping the
projects with ‘teacher_acctid’ and used it as an attribute
5. Total items per project- we calculated total number of items requested per each
project from the details provided in resources file and used it as an attribute
6. Cost of total items per project- we calculated total cost of items requested per each
project from the details provided in resources file and used it as an attribute
Several other derived attributes such as date, short description length has been considered
but they did not yield a significant performance improvement.
Model Selection and Evaluation
We have used three classifiers during the project. First we used decision tree classifier, then
we used logistic regression and finally we used SGD (stochastic gradient decent) classifier.
We started with tree classifier as it was easy to use. To evaluate the performance of
classifiers initially we used the cross validation technique. But later we realized that
competition is using ROC (area under the curve) score for evaluations. So we also used
ROC scores to evaluate the performance of the classifiers. As we had several choices for
classifiers we read several articles about the usage of classifiers. From them we realized
that decision tree normally does not perform well when there is data imbalance problem
and logistic regression was used instead of that.
Logistic regression was performed well with the given data and it achieved about 0.61 ROC
score. To improve the accuracy further more we used SGD classifier (logistic regression
with SGD training). On one hand it is more efficient than the logistic regression so that
predictions can be done in less amount of time. On the other hand it achieved higher
accuracy than the regression classifier. With default parameters for SGD classifier we were
able to achieve about 0.635 ROC score. To tune up the SGD classifier (to find best values
7. for the parameters) we performed a grid search and found optimum values for the number
of iterations, penalty, shuffle and alpha parameters. Using those values we were able
improve the accuracy up to 0.64 ROC score.
Ensemble Methods
We tried to use boosting algorithm to improve the performance of classifier. Among the
methods available we used “ada boost” method (AdaBoostClassifier) for that.
Implementation provided by scikit library only supports decision tree classifier and SGD
classifier. So we were not able to use logistic regression directly. Instead we tried to use
SGD classifier with boosting algorithm. But accuracy was increased only by an
insignificant amount.
Further Improvements
Essays data contains huge amount of data but they were not used during the predictions
apart from the essay length. We tried to extract essay data using TfidVectorizer but it was
not successful due to memory constraints. As an alternative we tried hashing methods but it
reduced the accuracy of the essay data. We think that accuracy of the classifier may
improve further if some features from the essay data are included in training data. Also use
of ensemble methods will definitely improve the accuracy of predications.
Support Libraries Used
We used ‘Pandas’ data analysis library to generate data frames from the provided comma-
separated values files which can be used with other data analysis and modeling tools which
we used. Other than that we used functions provided with ‘Pandas’ library for generating
bins in order to discretize the attributes with less repetitive values and merging data frames
from several data sources.
Then we used ‘NumPy’ extension library in order to generate multidimensional arrays
using ‘Pandas’ data frames and data series to make it easy to access certain ranges of data
(i.e. separate the indices of training data set from test data set) and locate some properties of
data like median and quartiles. Also when combining derived attributes with existing
attributes functions provided with ‘NumPy’ library was useful.
‘Scikit-learn’ machine learning library was the library we used to integrate data analysis,
preprocessing, classification, regression and modeling tools into our implementations. From
the various tools provided with ‘Scikit-learn’ library we used preprocessing tools like
‘Label Encoder’ and ‘One Hot Encoder’, ‘Standard Scalar’ and text feature extraction tools
classification tools like ‘Decision Tree Classifier’, ‘SGD Classifier’ and ‘Logistic
Regression’, model selection and evaluation tools like ‘Grid Search’, ensemble tools like
‘AdaBoost Classifier’ and metrics like ‘roc_auc_score’ to compute area under the curve
(AUC) from prediction scores as mentioned above.