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Mobile Computing Algorithms and Systems for User-Aware Optimization of Enterprise Applications

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Mobile Computing Algorithms and Systems for User-Aware Optimization of Enterprise Applications MOBILE COMPUTING ALGORITHMS AND SYSTEMS FOR USER-AWARE OPTIMIZATION OF ENTERPRISE APPLICATIONS A Dissertation Presented to The Academic Faculty By Uma Parthavi Moravapalle In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Electrical and Computer Engineering Georgia Institute of Technology May 2019 Copyright c Uma Parthavi Moravapalle 2019 MOBILE COMPUTING ALGORITHMS AND SYSTEMS FOR USER-AWARE OPTIMIZATION OF ENTERPRISE APPLICATIONS Approved by: Prof. Raghupathy Sivakumar, Advisor School of Electrical and Computer Prof. Douglas Blough Engineering School of Electrical and Computer Georgia Institute of Technology Engineering Georgia Institute of Technology Prof. Faramarz Fekri School of Electrical and Computer Prof. Karthik Ramachandran Engineering Scheller College of Business Georgia Institute of Technology Georgia Institute of Technology Prof. Umakishore Ramachandran Dr. Shruti Sanadhya College of Computing Connectivity Georgia Institute of Technology Facebook Date Approved: March 7th 2019 All the power is within you. You can do anything. Swami Vivekananda To Amma and Nanna ... ACKNOWLEDGEMENTS I would like to express my appreciation to all the people who directly and indirectly helped me over the course of my PhD. I want to start this list by thanking my advisor Prof. Raghupathy Sivakumar. Siva was an amazing mentor and a great role model to me. Without his guidance, this dissertation would not have been possible. His breadth of knowledge and the clarity on research that comes with this will always inspire me. I learnt to emphasize practicality and relevance of solutions from him. He encouraged me to believe in myself and instilled confidence when I was unsure. Most importantly, he is a kind and patient person who recognized my strengths and weaknesses and created the bandwidth for me to grow. I am extremely lucky to have Siva as an advisor. I want to express my gratitude to Shruti Sanadhya for her mentorship. She was always available for advice when I needed it. I also want to thank my other labmates - Chao- fang Shih, Bhuvana Krishnaswamy, Yubing Jian, Mohit Agarwal, and Shruti Lall for their support over the years with their encouragement, brainstorming, feedback and friendship. Lonely lunchtimes and boring weekends were rarely an issue. Even the tiniest success I have seen would not have been possible if not for my family’s whole-hearted and unwavering support. I would like to thank my father (Mohan Reddy), my mother (Rajani), my brother (Nanda Kishore), my sister-in-law (Harshita) and my hus- band (Anirrudh) for their constant prayers and encouragement. I would like to thank my committee members - Prof. Umakishore Ramachandran, Prof. Faramarz Fekri, Prof. Karthik Ramachandran, Prof. Dough Blough, and Dr. Shruti Sanadhya for their valuable comments and feedback in shaping this dissertation. Finally, I would like to thank the lord almighty for making it possible. v TABLE OF CONTENTS Acknowledgments . v List of Tables . xi List of Figures . xii Summary . xvi Chapter 1: Introduction . 1 1.1 Enterprise Application Mobilization . 3 1.1.1 User-Aware enterprise mobility . 5 1.1.2 Enterprise Mobility Architecture and Mobilization Challenges . 6 1.2 Research Focus . 8 1.3 Thesis statement . 12 1.4 Thesis Organization . 12 Chapter 2: Literature Survey . 14 2.1 Content Sharing . 14 2.1.1 Commercial Solutions: . 14 2.1.2 Research Solutions: . 14 2.2 Workflow Mobilization . 16 vi 2.2.1 Commercial Solutions . 16 2.2.2 Research Solutions . 17 2.3 Front-end APIfication . 18 2.4 Collaboration . 19 Chapter 3: Dejavu: Assisted Email Replies For Reduction Of Reply Burden On Smartphones . 20 3.1 Motivation . 22 3.1.1 Datasets . 22 3.1.2 Processing . 24 3.1.3 Methodology . 25 3.1.4 Metrics: . 25 3.1.5 Analysis . 28 3.1.6 Insights . 29 3.2 The DejaVu Solution . 30 3.2.1 Problem Definition and Scope . 30 3.2.2 The DejaVu solution . 31 3.3 Dejavu++: Optimizations to Dejavu ..................... 38 3.3.1 Reduction of Computation Complexity With Topic Filters . 39 3.3.2 Improving the relevancy of suggestions with user feedback . 45 3.3.3 Expanding the sources of suggestions to the global network of mail- boxes . 47 3.3.4 Architecture . 48 3.3.5 Prototype . 50 vii 3.4 Evaluation . 52 3.4.1 Methodology . 52 3.4.2 Macroscopic Results . 53 3.4.3 Microscopic Results . 55 3.4.4 User burden reduction . 56 3.4.5 Performance Comparison to Related Approaches . 58 3.4.6 Performance of Dejavu++ ...................... 60 Chapter 4: Taskr: Fast and Easy Mobilization of Spot Tasks in Enterprise Web Application . 68 4.1 Introduction . 68 4.2 Mobilization and Spot Tasks . 70 4.2.1 Mobilization and Defeaturization . 70 4.2.2 Spot Tasks . 73 4.3 Taskr: A Do-it-Yourself Approach to Spot Task Mobilization . 76 4.3.1 Key Design Elements . 77 4.3.2 Challenges and Design Choices . 81 4.4 Evaluation . 94 Chapter 5: Trackr: Reliable Tracking of UI Elements within Web Applications to Enable Robust APIfication . 100 5.1 Introduction . 100 5.2 Background and Motivation . 103 5.2.1 Web Applications and DOM Trees: A Primer . 103 5.2.2 Problem Definition, Scope, and Goals . 106 viii 5.2.3 Problem Relevance and Significance . 107 5.2.4 Related Approaches and Performance Analysis . 109 5.3 Trackr: Fingerprinting Algorithm . 112 5.3.1 Architecture Overview . 112 5.3.2 Quorum Fingerprinting . 113 5.3.3 Fingerprinting Optimizations . 115 5.4 Evaluation . 121 5.4.1 Prototype: . 121 5.5 Use Cases . 128 5.5.1 Automation: . 128 5.6 Issues . 133 Chapter 6: Peek: A mobile-to-mobile remote computing protocol . 135 6.1 Introduction . 135 6.2 Background and Motivation . 138 6.2.1 A Primer: . 138 6.2.2 A case for mobile-mobile remote computing . 138 6.2.3 Key Challenges . 140 6.3 PEEK: A mobile-to-mobile remote computing protocol . 144 6.3.1 Multi-touch Support and Context Association: . 144 6.3.2 Multi-modal Compression: . 146 6.3.3 System Architecture . 148 6.4 Evaluation . 149 ix Chapter 7: Integrated Operations . 154 7.0.1 At the enterprise . 154 7.0.2 At the smartphone . 156 Chapter 8: Future Work . 157 8.1 Automated reply suggestions . 157 8.2 Do-it-yourself application mobilization . 158 8.3 Robust front-end APIfication . 160 8.4 Mobile-to-Mobile Remote computing for smartphones . 161 Chapter 9: Conclusions . 162 References . 176 x LIST OF TABLES 3.1 The AVOCADO dataset . 23 3.2 ENRON dataset . 24 3.3 Example matching email snippets for a user in ENRON dataset . 26 3.4 List of stopwords filtered by Dejavu ..................... 35 3.5 Examples of email snippets . 57 4.1 Percentage of action elements with associated labels . 86 4.2 Different UI frameworks used by enterprise applications . 86 4.3 List of Worflows configured on enterprise applications . 94 5.1 Default Experimental Parameters . 123 5.2 Effect of different optimizations on Trackr . 125 6.1 Touch to mouse translation . 140 6.2 Non intuitive and non existent gestures . 141 6.3 VNC compression on smartphones . 142 6.4 Action descriptions . 149 xi LIST OF FIGURES 1.1 Enterprise Mobility Strategies . ..
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