| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 11, 2016 |
| Latest Amendment Date: | August 11, 2016 |
| Award Number: | 1629791 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Maria Zemankova
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2016 |
| End Date: | June 30, 2018 (Estimated) |
| Total Intended Award Amount: | $100,000.00 |
| Total Awarded Amount to Date: | $100,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
520 LEE ENTRANCE STE 211 AMHERST NY US 14228-2577 (716)645-2634 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
338 Davis Hall Buffalo NY US 14260-2500 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CCRI-CISE Cmnty Rsrch Infrstrc |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The world's 2 billion smartphones and 4 million apps have become a large part of most people's communication and computing experiences. Most apps need to store structured data like contacts, address book entries, or even your top scores in Candy Crush. Most smartphones include a piece of software called an embedded database that helps apps save and access this information. For example, both Android phones and iPhones include a popular embedded database called SQLite. SQLite is used a lot -- the project team measured nearly 180 thousand SQLite requests per day on an average Android phone in real-world use. The team also found that lots of these requests use SQLite inefficiently, resulting in shorter battery life, slower performance, and potentially wasted plan data. To do better, research is need to understand what is the problems are. As part of this project, the team is gathering the real-world data and building the infrastructure that researchers will need to make better embedded databases, leading to longer battery life, more responsive phones, and longer-lasting plan data.
This is the planning stage of an overarching project that aims to make it easier for researchers to explore the challenges of "Pocket-Scale" data. While server-class database systems are frequently tested and tuned for continuous high-throughput query processing, embedded databases experience lower-volume but bursty workloads produced by interactive use. These workloads are far more dynamic, making them much harder to reproduce, which in turn make it hard to evaluate new techniques and systems in realistic settings. The ultimate goal for this community infrastructure (CI) project is to establish a self-sustaining community of researchers and industrial partners who create and share workload traces, benchmarks, instrumentation, and visualization tools, making it easier to evaluate innovations in embedded database technology. In this CI planning stage, the project team is building up the community by reaching out to potential partners from multiple areas including databases, mobile systems, programming languages, and internet-of-things. As a hub for this community, the team is developing a toolchain for benchmarking embedded database, as well as a community website hosted at the University at Buffalo (http://pocketdata.info). The project also involves outreach efforts including tutorials, workshops, and seminar series. With the interest and support from the community, the embedded database benchmark CI is expected to spark a new wave of research interest on pocket-scale data management.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Over a decade ago, the world entered the era of Big Data by challenging common assumptions. We stopped thinking of distributed systems failures as outliers or corner cases. We stopped thinking that we couldn’t collect everything. We stopped thinking that limits on data sizes would prevent us from distinguishing between signal and noise. Now, instead of big databases, many people have database engines in their pockets, refrigerators, televisions, and even light bulbs. These databases are used to track everything from schedules, to home energy usage, to personal health, and more. It's important that we create the tools that allow these databases to work well (and without lagging up your phone).
This project built the tools and technical expertise required to understand how databases work on Pocket Scale devices. We have developed new techniques for measuring performance designed for mobile devices (rather than big-data servers). We have identified opportunities for research on mobile data management including areas where databases can be made to work with, rather than against mobile operating system power management features. We have started building a community through a panel discussion, and interactions with mobile database developers and experts in the database benchmarking community. We have used real-world data to make sure that the benchmarks that we create are representative. The datasets that we collected have also been used by the creators of SQLite, who expressed that our study provided them with the "typical" usage information that they were not in a position to collect otherwise.
Last Modified: 10/29/2018
Modified by: Oliver A Kennedy
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