Ethno-Mining: Integrating Words and Numbers from the Ground Up

In this paper we present ethno-mining, a mixed methods approach drawing on techniques from ethnography and data mining. Ethno-mining is characterized by tight, iterative loops that integrate both the results and the processes of ethnographic and data mining techniques to interpret data. Ethno-mining provides two key benefits. First, it makes use of both qualitative and quantitative data (e.g. observations and sensor data) to study phenomena that are practically inaccessible through either data type alone. Second, it provides a means of interpreting that data which produces novel insights by exposing the biases inherent in either type of data alone. We present ethno-mining in the context of a study of mobility and laptop use in the home, discussing how findings from the study relate to the use of the method. Author

[1]  E. Goffman Behavior in public places : notes on the social organization of gatherings , 1964 .

[2]  Paul Dourish,et al.  Re-place-ing space: the roles of place and space in collaborative systems , 1996, CSCW '96.

[3]  Robert Axelrod,et al.  Advancing the art of simulation in the social sciences , 1997, Complex..

[4]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[5]  S. Hormuth The sampling of experiences in situ , 1986 .

[6]  Miguel P Caldas,et al.  Research design: qualitative, quantitative, and mixed methods approaches , 2003 .

[7]  Allison Woodruff,et al.  Portable, But Not Mobile: A Study of Wireless Laptops in the Home , 2007, Pervasive.

[8]  A. Kellerman,et al.  The Constitution of Society : Outline of the Theory of Structuration , 2015 .

[9]  T. Jick Mixing Qualitative and Quantitative Methods: Triangulation in Action. , 1979 .

[10]  Kenneth T. Anderson,et al.  D ESIGN M ANAGEMENT J OURNAL Design Ethnography , 1999 .

[11]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[12]  Svetha Venkatesh,et al.  Recognition of emergent human behaviour in a smart home: A data mining approach , 2007, Pervasive Mob. Comput..

[13]  Morgan G. Ames,et al.  The Social Life of Cameraphone Images , 2007 .

[14]  A. Strauss,et al.  Basics of Qualitative Research , 1992 .

[15]  Allison Woodruff,et al.  A Quantitative Method for Revealing and Comparing Places in the Home , 2006, UbiComp.

[16]  Steve Benford,et al.  Supporting ethnographic studies of ubiquitous computing in the wild , 2006, DIS '06.

[17]  Sunita Sarawagi,et al.  User-Adaptive Exploration of Multidimensional Data , 2000, VLDB.

[18]  Gregory D. Abowd,et al.  Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones , 2006, UbiComp.

[19]  Jennifer Mankoff,et al.  When participants do the capturing: the role of media in diary studies , 2005, CHI.

[20]  Emmanuel,et al.  Activity recognition in the home setting using simple and ubiquitous sensors , 2003 .

[21]  Allison Woodruff,et al.  Maps of Our Lives : Sensing People and Objects Together in the Home , 2005 .

[22]  Deborah D. Heisley,et al.  Autodriving: A Photoelicitation Technique , 1991 .

[23]  Daisy Zhe Wang,et al.  Probabilistic Data Management for Pervasive Computing: The Data Furnace Project , 2006, IEEE Data Eng. Bull..

[24]  Gregory D. Abowd,et al.  PowerLine Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use , 2006, UbiComp.