Proceedings of the 9th Annual ACM India Conference最新文献_第2页

Semantic Clustering Driven Approaches to Recommender Systems 推荐系统的语义聚类驱动方法

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998487

P. Bafna, S. Shirwaikar, Dhanya Pramod

引用次数: 2

Role of Reduced Inputs in Flag Mining 减少投入在标志挖掘中的作用

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998485

Rishab Bansal, A. Ravindar

{"title":"Role of Reduced Inputs in Flag Mining","authors":"Rishab Bansal, A. Ravindar","doi":"10.1145/2998476.2998485","DOIUrl":"https://doi.org/10.1145/2998476.2998485","url":null,"abstract":"Typically compilers provide a wide choice of optimization flags that can be used to improve the application performance. The process of searching for the best flag combination for a given application is referred to Flag Mining. Brute force ways of flag mining are time consuming as it requires a number of runs with different combinations of flags. Flag mining techniques that are based on machine learning rely on a database consisting of measurements of application run-times obtained with a large number of combinations of binaries compiled with different flags. This work quantifies the impact of using reduced inputs in flag mining. Reduced inputs are much smaller inputs than real representative inputs and cause the application to run for less than 10 percent of original execution time. Some examples of reduced inputs are the train input used in SPEC benchmarks, MinneSPEC inputs. Using reduced inputs instead of full inputs would reduce time/space overhead of flag mining significantly when used in brute force or machine learning based methods. However inorder to use reduced inputs for flag mining, the behavior of the application compiled with a set of flags, when presented with reduced inputs should give similar benefits on full representative inputs. This can happen only if reduced inputs are an accurate representatives of ref inputs in the context of application performance. Our experiments show that reduced inputs correlate to full representative inputs for 5 out of 7 SPEC CPU2006 benchmarks on all 11 flag combinations considered with the GCC compiler and are found to reduce the experimentation time of flag mining by up to 82%. We also outline the necessary conditions that need to be satisfied by reduced inputs to qualify for use in flag mining.","PeriodicalId":171399,"journal":{"name":"Proceedings of the 9th Annual ACM India Conference","volume":"12 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SkipLPA: An Efficient Label Propagation Algorithm for Community Detection in Sparse Network 一种用于稀疏网络社区检测的高效标签传播算法SkipLPA

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998486

Sanjay B. Thakare, Arvind W. Kiwelekar

引用次数: 7

Efficient Multi-depth Querying on Provenance of Relational Queries Using Graph Database 基于图数据库的关系查询来源的高效多深度查询

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998480

A. Rani, Navneet Goyal, S. Gadia

{"title":"Efficient Multi-depth Querying on Provenance of Relational Queries Using Graph Database","authors":"A. Rani, Navneet Goyal, S. Gadia","doi":"10.1145/2998476.2998480","DOIUrl":"https://doi.org/10.1145/2998476.2998480","url":null,"abstract":"Data Provenance is the history associated with that data. It constitutes the origin, creation, processing, and archiving of data. In today's Internet era, it has gained significant importance for database analytics. Most of the provenance models store provenance information in relational databases for further querying and analysis. Although, querying of provenance in Relational Databases is very efficient for small data sets, it becomes inefficient as the provenance data grows and traversal depth of provenance query increases. This is mainly due to increase in number of join operations to search the entire provenance data. Graph Databases provide an alternative to RDBMSs for storing and analyzing provenance data as it can scale to billions of nodes and at the same time traverse thousands of relationships efficiently. In this paper, we propose efficient multi-depth querying of provenance data using graph databases. The proposed solution allows efficient querying of provenance of current as well as historical queries. A comparison between relational and graph databases is presented for varying provenance data size and traversal depths. Graph databases are found to scale well with increasing depth of provenance queries, whereas in relational databases the querying time increases exponentially.","PeriodicalId":171399,"journal":{"name":"Proceedings of the 9th Annual ACM India Conference","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128936526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Distributed Decision Tree 分布式决策树

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998478

Ankit Desai, S. Chaudhary

引用次数: 9

Service Demand Modeling and Prediction with Single-user Performance Tests 基于单用户性能测试的服务需求建模和预测

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998483

A. Kattepur, M. Nambiar

{"title":"Service Demand Modeling and Prediction with Single-user Performance Tests","authors":"A. Kattepur, M. Nambiar","doi":"10.1145/2998476.2998483","DOIUrl":"https://doi.org/10.1145/2998476.2998483","url":null,"abstract":"Performance load tests of online transaction processing (OLTP) applications are expensive in terms of manpower, time and costs. Alternative performance modeling and prediction tools are required to generate accurate outputs with minimal input sample points. Service Demands (time needed to serve 1 request at queuing stations) are typically needed as inputs by most performance models. However, as service demands vary as a function of workload, models that input singular service demands produce erroneous predictions. The alternative, which is to collect service demands at varying workloads, require time and resource intensive load tests to estimate multiple sample points -- this defeats the purpose of performance modeling for industrial use. In this paper, we propose a service demand model as a function of concurrency that can be estimated with a single-user performance test. Further, we analyze multiple CPU performance metrics (cache hits/misses, branch prediction, context switches and so on) using Principal Component Analysis (PCA) to extract a regression function of service demand with increasing workloads. We use the service demand models as input to performance prediction algorithms such as Mean Value Analysis (MVA), to accurately predict throughput at varying workloads. This service demand prediction model uses CPU hardware counters, which is used in conjunction with a modified version of MVA with single-user service demand inputs. The predicted throughput values are within 9% deviation with measurements procured for a variety of application/hardware configurations. Such a service demand model is a step towards reducing reliance on conventional load testing for performance assurance.","PeriodicalId":171399,"journal":{"name":"Proceedings of the 9th Annual ACM India Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126577572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Development of Indian Weighted Diabetic Risk Score (IWDRS) using Machine Learning Techniques for Type-2 Diabetes 使用机器学习技术开发印度加权糖尿病风险评分(IWDRS)用于2型糖尿病

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998497

Omprakash Chandrakar, Jatinderkumar R. Saini

引用次数: 22

Topical Authoritative Answerer Identification on Q&A Posts using Supervised Learning in CQA Sites CQA网站中使用监督学习的问答帖子的主题权威答疑人识别

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998490

T. P. Sahu, N. K. Nagwani, Shrish Verma

{"title":"Topical Authoritative Answerer Identification on Q&A Posts using Supervised Learning in CQA Sites","authors":"T. P. Sahu, N. K. Nagwani, Shrish Verma","doi":"10.1145/2998476.2998490","DOIUrl":"https://doi.org/10.1145/2998476.2998490","url":null,"abstract":"Community Question Answering (CQA) site is an online platform for hosting information in question-answer form by collaborative users worldwide. There are basically two types of user in this CQA sites: Asker -- who post their query as questions and Answerer -- who provide the answers to these questions. The semi-structured and growing size of contents in CQA sites is posing several challenges. As there is no restriction in posting the number of answers to a question, so the common challenge is to identify the authoritative answerers of a question in order to evaluate the answer quality for selecting the best answer. In this paper, we use latent dirichlet allocation (LDA) the statistical topic modelling on textual data and statistical computing on metadata to identify the features that would reflect the topical authoritative of answerer. Then these features are represented as vector for each answerer of the dataset under investigation for learning the classifier model. The various baseline classifier model are used to identify the topical authoritative answerer on Q&A posts of two real dataset extracted from StackOverflow and AskUbuntu. The correctness and effectiveness of classifier models are evaluated using various parameters like accuracy, precision, recall, and kappa statistic. The experimental result shows that Random Forest classifier outperforms over each evaluation parameter than other classification algorithms.","PeriodicalId":171399,"journal":{"name":"Proceedings of the 9th Annual ACM India Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130553558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

EEQuest: An Event Extraction and Query System 一个事件提取和查询系统

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998482

Prerit Jain, H. Bendapudi, Shrisha Rao

引用次数: 0

Search Based Test Data Generation: A Multi Objective Approach using MOPSO Evolutionary Algorithm 基于搜索的测试数据生成:一种基于MOPSO进化算法的多目标方法

Proceedings of the 9th Annual ACM India Conference Pub Date : 2016-10-21 DOI: 10.1145/2998476.2998492

P. Gopi, M. Ramalingam, C. Arumugam

引用次数: 3