PPT-Energy-Efficient Query Processing on

Embedded CPUGPU Architectures Xuntao Cheng Bingsheng He Chiew Tong Lau Nanyang Technological University Singapore 1 Outline Motivations System Design Evaluations Conclusion 2 Query Processing in the Era of IoT. Jingren Zhou. Microsoft Corp.. Large-scale Distributed Computing. Large data centers (x1000 machines): storage and computation. Key technology for search . (Bing, Google, Yahoo). Web data analysis, user log analysis, relevance studies, etc.. microsensor. networks. W. . Heinzelman. , A. . Chandrakasan. , H. . Balakrishnan. , Published in 2000. Overview. Problem Description. Energy Models. Conventional Methods. LEACH Algorithm. Conclusions. Abstract. With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed.. IDOL Under . the . Hood. Techniques and algorithms. #. SeizeTheData. Read the presenter notes before presenting.. Most of the content of this presentation is contained therein.. IDOL knowledge flow. Acquisition. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. John . Sirmon. Senior Escalation Engineer – Microsoft Corporation. Microsoft CSS at PASS 2009. Session Objectives And Takeaways. Processing issues . When good cubes go bad. Troubleshooting processing simplified . of Massive Trajectory Data based on MapReduce. Qiang. Ma, Bin Yang (. Fudan. University). Weining. . Qian. , . Aoying. Zhou (ECNU). Presented By: . Xin. Cao (Aalborg University). Outline. Introduction . CSE 8330. Conventional Query Processing. Statistics generation. Query optimization. Query execution. Unreliable cardinality . e. stimates. Complex queries. Changes in the runtime environment. Query interactivity. FAWN. :. Workloads and Implications. Vijay . Vasudevan. , David Andersen, Michael . Kaminsky. *, Lawrence Tan, . Jason Franklin. , . Iulian. . Moraru. Carnegie Mellon University, *Intel Labs Pittsburgh. Energy Efficient Operations Presentation to ASHRAE Energy Expo October 2 , 2013 Center for Energy and Environment During commissioning projects the commissioning agent develops a deep understanding of the facility &. . Energy efficiency Data Protocol and Portal (. EeDaPP. ). &. Energy efficient Mortgage Market Implementation Plan (. EeMMIP. ). In the EU 2. 7. there are… . Bring Energy Efficiency to the EU buildings stock: . Adapted from a talk by. : . Sapna. Jain & R. . Gokilavani. Some slides taken from . Jingren. Zhou's talk on Scope :. isg.ics.uci.edu/slides/MicrosoftSCOPE.pptx. Map-reduce framework. Good abstraction of group-by-aggregation operations. Vida . Rozite. . The Role of Standardization for Smart Grids in Realizing Their Energy-Efficiency Potential and Their Enabling Effect in Developing Access to Electricity in the Third World. Benefits . Query Processing. Document-at-a-time. Calculates complete scores for documents by processing all term lists, one document at a time. Term-at-a-time. Accumulates scores for documents by processing term lists one at a time.