Alexander Markowetz

Text vs. space: efficient geo-search query processing

Many web search services allow users to constrain text queries to a geographic location (e.g., yoga classes near Santa Monica). Important examples include local search engines such as Google Local and location-based search services for smart phones. Several research groups have studied the efficient execution of queries mixing text and geography; their approaches usually combine inverted lists with a spatial access method such as an R-tree or space-filling curve. In this paper, we take a fresh look at this problem. We feel that previous work has often focused on the spatial aspect at the expense of performance considerations in text processing, such as inverted index access, compression, and caching. We describe new and existing approaches and discuss their different perspectives. We then compare their performance in extensive experiments on large document collections. Our results indicate that a query processor that combines state-of-the-art text processing techniques with a simple coarse-grained spatial structure can outperform existing approaches by up to two orders of magnitude. In fact, even a naive approach that first uses a simple inverted index and then filters out any documents outside the query range outperforms many previous methods.

Psycho-Informatics: Big Data shaping modern psychometrics

For the first time in history, it is possible to study human behavior on great scale and in fine detail simultaneously. Online services and ubiquitous computational devices, such as smartphones and modern cars, record our everyday activity. The resulting Big Data offers unprecedented opportunities for tracking and analyzing behavior. This paper hypothesizes the applicability and impact of Big Data technologies in the context of psychometrics both for research and clinical applications. It first outlines the state of the art, including the severe shortcomings with respect to quality and quantity of the resulting data. It then presents a technological vision, comprised of (i) numerous data sources such as mobile devices and sensors, (ii) a central data store, and (iii) an analytical platform, employing techniques from data mining and machine learning. To further illustrate the dramatic benefits of the proposed methodologies, the paper then outlines two current projects, logging and analyzing smartphone usage. One such study attempts to thereby quantify severity of major depression dynamically; the other investigates (mobile) Internet Addiction. Finally, the paper addresses some of the ethical issues inherent to Big Data technologies. In summary, the proposed approach is about to induce the single biggest methodological shift since the beginning of psychology or psychiatry. The resulting range of applications will dramatically shape the daily routines of researches and medical practitioners alike. Indeed, transferring techniques from computer science to psychiatry and psychology is about to establish Psycho-Informatics, an entire research direction of its own.

Recorded Behavior as a Valuable Resource for Diagnostics in Mobile Phone Addiction: Evidence from Psychoinformatics.

Psychologists and psychiatrists commonly rely on self-reports or interviews to diagnose or treat behavioral addictions. The present study introduces a novel source of data: recordings of the actual problem behavior under investigation. A total of N = 58 participants were asked to fill in a questionnaire measuring problematic mobile phone behavior featuring several questions on weekly phone usage. After filling in the questionnaire, all participants received an application to be installed on their smartphones, which recorded their phone usage for five weeks. The analyses revealed that weekly phone usage in hours was overestimated; in contrast, numbers of call and text message related variables were underestimated. Importantly, several associations between actual usage and being addicted to mobile phones could be derived exclusively from the recorded behavior, but not from self-report variables. The study demonstrates the potential benefit to include methods of psychoinformatics in the diagnosis and treatment of problematic mobile phone use.

The importance of analogue zeitgebers to reduce digital addictive tendencies in the 21st century

Analogue zeitgebers such as wristwatches and alarm clocks are essential for structuring everyday life. Since the dawn of the digital revolution – particularly since the advent of the smartphone – mobile phones have increasingly replaced analogue zeitgebers as a means of telling time. This functionality may prove problematic, in that it may contribute to the overuse of digital media (e.g. when checking the time turns into extended use of other smartphone utilities, including Internet-based applications). Of N = 3084 participants, 45% reported wearing a wristwatch and 67% used an analogue alarm clock. We observed that participants who reported using analogue zeitgebers used their mobile-/smartphone significantly less. Use of analogue zeitgebers may prove a practical tool for therapeutic and preventative interventions for problematic Internet use in an increasingly digital age.

Toward Psychoinformatics: Computer Science Meets Psychology

The present paper provides insight into an emerging research discipline called Psychoinformatics. In the context of Psychoinformatics, we emphasize the cooperation between the disciplines of psychology and computer science in handling large data sets derived from heavily used devices, such as smartphones or online social network sites, in order to shed light on a large number of psychological traits, including personality and mood. New challenges await psychologists in light of the resulting “Big Data” sets, because classic psychological methods will only in part be able to analyze this data derived from ubiquitous mobile devices, as well as other everyday technologies. As a consequence, psychologists must enrich their scientific methods through the inclusion of methods from informatics. The paper provides a brief review of one area of this research field, dealing mainly with social networks and smartphones. Moreover, we highlight how data derived from Psychoinformatics can be combined in a meaningful way with data from human neuroscience. We close the paper with some observations of areas for future research and problems that require consideration within this new discipline.

Facebook usage on smartphones and gray matter volume of the nucleus accumbens

&NA; A recent study has implicated the nucleus accumbens of the ventral striatum in explaining why online‐users spend time on the social network platform Facebook. Here, higher activity of the nucleus accumbens was associated with gaining reputation on social media. In the present study, we touched a related research field. We recorded the actual Facebook usage of N = 62 participants on their smartphones over the course of five weeks and correlated summary measures of Facebook use with gray matter volume of the nucleus accumbens. It appeared, that in particular higher daily frequency of checking Facebook on the smartphone was robustly linked with smaller gray matter volumes of the nucleus accumbens. The present study gives additional support for the rewarding aspects of Facebook usage. Moreover, it shows the feasibility to include real life behavior variables in human neuroscientific research.

Keyword search over relational tables and streams

Relational Keyword Search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this article we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) We provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based, that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets.

Differentiating smartphone users by app usage

Tracking users across websites and apps is as desirable to the marketing industry as it is unalluring to users. The central challenge lies in identifying users from the perspective of different apps/sites. While there are methods to identify users via technical settings of their phones, these are prone to countermeasures. Yet, in this paper, we show that it is possible to differentiate users via their set of used apps, their app signature. To this end, we investigate the app usage of 46726 participants from the Menthal project. Even limiting our observation to the 500 globally most frequent apps results in unique signatures for 99.67% of users. Furthermore, even under this restriction, the average minimum Hamming distance to the closest other user is 25.93. Avoiding identification would thus require a massive change in the behavior of a user. Indeed, 99.4% of all users have unique usage patterns among the top 60 globally used apps. In contrast to previous work, this paper differentiates between users based on behavior instead of technical parameters. It thus opens an entirely new discussion regarding privacy.

How age and gender affect smartphone usage

Smartphone usage is a hot topic in pervasive computing due to their popularity and personal aspect. We present our initial results from analyzing how individual differences, such as gender and age, affect smartphone usage. The dataset comes from a large scale longitudinal study, the Menthal project. We select a sample of 30, 677 participants, from which 16, 147 are males and 14, 523 are females, with a median age of 21 years. These have been tracked for at least 28 days and they have submitted their demographic data through a questionnaire. The ongoing experiment has been started in January 2014 and we have used our own mobile data collection and analysis framework. Females use smartphones for longer periods than males, with a daily mean of 166.78 minutes vs. 154.26 minutes. Younger participants use their phones longer and usage is directed towards entertainment and social interactions through specialized apps. Older participants use it less and mainly for getting information or using it as a classic phone.

Contributing to Overall Life Satisfaction: Personality Traits Versus Life Satisfaction Variables Revisited—Is Replication Impossible?

Virtually everybody would agree that life satisfaction is of immense importance in everyday life. Thus, it is not surprising that a considerable amount of research using many different methodological approaches has investigated what the best predictors of life satisfaction are. In the present study, we have focused on several key potential influences on life satisfaction including bottom-up and top-down models, cross-cultural effects, and demographic variables. In four independent (large scale) surveys with sample sizes ranging from N = 488 to 40,297, we examined the associations between life satisfaction and various related variables. Our findings demonstrate that prediction of overall life satisfaction works best when including information about specific life satisfaction variables. From this perspective, satisfaction with leisure showed the highest impact on overall life satisfaction in our European samples. Personality was also robustly associated with life satisfaction, but only when life satisfaction variables were not included in the regression model. These findings could be replicated in all four independent samples, but it was also demonstrated that the relevance of life satisfaction variables changed under the influence of cross-cultural effects.