ABSTRACT
Effective use of personal data is a core utility of modern smartphones. On Android, several challenges make developing compelling personal data applications difficult. First, personal data is stored in isolated silos. Thus, relationships between data from different providers are missing, data must be queried by source of origin rather than meaning and the persistence of different types of data differ greatly. Second, interfaces to these data are inconsistent and complex. In turn, developers are forced to interleave SQL with Java boilerplate, resulting in error-prone code that does not generalize. Our solution is Epistenet: a toolkit that (1) unifies the storage and treatment of mobile personal data; (2) preserves relationships between disparate data; (3) allows for expressive queries based on the meaning of data rather than its source of origin (e.g., one can query for all communications with John while at the park); and, (4) provides a simple, native query interface to facilitate development.
- P. Bonnet, J. Gehrke, and P. Seshadri. 2000. Querying the physical world. IEEE Personal Communications 7, 5: 10--15. http://doi.org/10.1109/98.878531Google ScholarCross Ref
- Philippe Bonnet, Johannes Gehrke, and Praveen Seshadri. 2001. Towards Sensor Database Systems. Proc. MDM'01, 3--14. Google ScholarDigital Library
- Carlo Curino, Matteo Giani, Marco Giorgetta, Alessandro Giusti, Amy L. Murphy, and Gian Pietro Picco. 2005. Mobile Data Collection in Sensor Networks': The TinyLime Middleware. Elsevier Pervasive and Mobile Computing Journal 4: 446--469. Google ScholarDigital Library
- Mathieu d'Aquin, Andriy Nikolov, and Enrico Motta. 2011. Building SPARQL-Enabled Applications with Android devices. Proc. ISWC'11.Google Scholar
- Sauvik Das, LaToya Green, Beatrice Perez, Michael Murphy, and Adrian Perrig. 2010. Detecting User Activities Using the Accelerometer on Android Smartphones. TRUST-REU Tech ReportsGoogle Scholar
- Sauvik Das, Eiji Hayashi, and Jason Hong. 2013. Exploring Capturable Everyday Memory for Autobiographical Authentication. Proc. UbiComp'13. Google ScholarDigital Library
- Jerome David and Jerome Euzenat. 2010. Linked data from your pocket. Proc. ISWC'10 Demo Track.Google Scholar
- Anind K. Dey. 2001. Understanding and Using Context. Personal and Ubiq. Computing 5, 1: 4--7. Google ScholarDigital Library
- Eric Freeman and David Gelernter. 1996. Lifestreams. ACM SIGMOD Record 25, 1: 80--86. Google ScholarDigital Library
- David Gelernter. 1985. Generative communication in Linda. ACM TOPLAS 7, 1: 80--112. Google ScholarDigital Library
- Alina Hang, Alexander De Luca, and Heinrich Hussman. 2015. I Know What You Did Last Week! Do You? Dynamic Security Questions for Fallback Authentication on Smartphones. Proc. CHI'15. Google ScholarDigital Library
- A. M. Khan, Y.-K. Lee, S. Y. Lee, and T.-S. Kim. 2010. Human Activity Recognition via an Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis. Proc. FIT'10, 1--6.Google ScholarCross Ref
- Andy J. Ko, Brad A. Myers, and Htet Htet Aung. 2004. Six Learning Barriers in End-User Programming Systems. Proc. VLHCC'04, 199--206. Google ScholarDigital Library
- Andy J. Ko and Brad A. Myers. 2003. Development and evaluation of a model of programming errors. Proc. HCC'03, 7--14. Google ScholarDigital Library
- Marc Langheinrich. 2001. Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems. Proc. Ubicomp'01, 273--291. Google ScholarDigital Library
- Danh Le-Phuoc, Josiane Xavier Parreira, Vinny Reynolds, and Manfred Hauswirth. 2010. RDF on the go: An RDF storage and query processor for mobile devices. Proc. CEUR Workshops 658: 149--152. Google ScholarDigital Library
- Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. 2003. The design of an acquisitional query processor for sensor networks. Proc. SIGMOD '03, ACM Press, 491--502. Google ScholarDigital Library
- E. Miller. 1998. An Introduction to the Resource Description Framework. D-Lib Magazine.Google Scholar
- Suman Nath. 2012. ACE. Proc. MobiSys'12, ACM Press, 29--42. Google ScholarDigital Library
- Natalya F. Noy and Deborah L. Mcguinness. 2000. Ontology Development 101: A Guide to Creating Your First Ontology. SKSL Technical Report KSL-01-05, 1--25.Google Scholar
- H. Tangmunarunkit, J. Kang, Z. Khalapyan, et al. 2015. Ohmage. ACM Transactions on Intelligent Systems and Technology 6, 3: 1--21.Google ScholarDigital Library
- Roberto Yus, Carlos Bobed, Guillermo Esteban, and Fernando Bobillo. 2013. Android goes Semantic: DL Reasoners on Smartphones.Google Scholar
- Cover Lock Screen. Retrieved from https://play.google.com/store/apps/details?id=com.coverscreen.cover&hl=enGoogle Scholar
- Yahoo Aviate Launcher. Retrieved from https://play.google.com/store/apps/details?id=com.tul.aviate&hl=enGoogle Scholar
- RescueTime. Retrieved from https://rescuetime.comGoogle Scholar
- Moves App. Retrieved from https://moves-app.comGoogle Scholar
- SPARQL. Retrieved from http://www.w3.org/2009/sparql/wiki/Main_PageGoogle Scholar
- Content Provider. Retrieved from http://developer.android.com/guide/topics/providers/content-providers.htmlGoogle Scholar
- SAGA. Retrieved from http://www.getsaga.comGoogle Scholar
- If This Then That. Retrieved from https://ifttt.comGoogle Scholar
- Power Profile for Android. Retrieved from https://source.android.com/devices/tech/power.htmlGoogle Scholar
Index Terms
- Epistenet: facilitating programmatic access & processing of semantically related mobile personal data
Recommendations
Alliance Rules for Data Warehouse Cleansing
ICSPS '09: Proceedings of the 2009 International Conference on Signal Processing SystemsData Cleansing is an activity performed on the data sets of data warehouse to enhance and maintain the quality and consistency of the data. This paper addresses the problems related with dirty data, entrance of dirty data and detection of dirty data in ...
Comments