Jaya Kawale

Churn Prediction in MMORPGs: A Social Influence Based Approach

Massively Multiplayer Online Role Playing Games(MMORPGs) are computer based games in which players interactwith one another in the virtual world. Worldwide revenuesfor MMORPGs have seen amazing growth in last few years and itis more than a 2 billion dollars industry as per current estimates.Huge amount of revenue potential has attracted several gamingcompanies to launch online role playing games. One of the majorproblems these companies suffer apart from fierce competitionis erosion of their customer base. Churn is a big problem for thegaming companies as churners impact negatively in the wordof-mouth reports for potential and existing customers leading tofurther erosion of user base.We study the problem of player churn in the popularMMORPG EverQuest II. The problem of churn predictionhas been studied extensively in the past in various domainsand social network analysis has recently been applied to theproblem to understand the effects of the strength of social tiesand the structure and dynamics of a social network in churn.In this paper, we propose a churn prediction model based onexamining social influence among players and their personalengagement in the game. We hypothesize that social influence is avector quantity, with components negative influence and positiveinfluence. We propose a modified diffusion model to propagatethe influence vector in the players network which represents thesocial influence on the player from his network. We measure aplayers personal engagement based on his activity patterns anduse it in the modified diffusion model and churn prediction. Ourmethod for churn prediction which combines social influenceand player engagement factors has shown to improve predictionaccuracy significantly for our dataset as compared to predictionusing the conventional diffusion model or the player engagementfactor, thus validating our hypothesis that combination of boththese factors could lead to a more accurate churn prediction.

Testing the significance of spatio-temporal teleconnection patterns

Dipoles represent long distance connections between the pressure anomalies of two distant regions that are negatively correlated with each other. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Nino climate phenomenon is known to be responsible for precipitation and temperature anomalies over large parts of the world. Systematic approaches for dipole detection generate a large number of candidate dipoles, but there exists no method to evaluate the significance of the candidate teleconnections. In this paper, we present a novel method for testing the statistical significance of the class of spatio-temporal teleconnection patterns called as dipoles. One of the most important challenges in addressing significance testing in a spatio-temporal context is how to address the spatial and temporal dependencies that show up as high autocorrelation. We present a novel approach that uses the wild bootstrap to capture the spatio-temporal dependencies, in the special use case of teleconnections in climate data. Our approach to find the statistical significance takes into account the autocorrelation, the seasonality and the trend in the time series over a period of time. This framework is applicable to other problems in spatio-temporal data mining to assess the significance of the patterns.

Different Modes of Variability over the Tasman Sea: Implications for Regional Climate*

AbstractA new approach is used to detect atmospheric teleconnections without being bound by orthogonality (such as empirical orthogonal functions). This method employs negative correlations in a global dataset to detect potential teleconnections. One teleconnection occurs between the Tasman Sea and the Southern Ocean. It is related to El Nino–Southern Oscillation (ENSO), the Indian Ocean dipole (IOD), and the southern annular mode (SAM). This teleconnection is significantly correlated with SAM during austral summer, fall, and winter, with IOD during spring, and with ENSO in summer. It can thus be described as a hybrid between these modes. Given previously found relationships between IOD and ENSO, and IOD’s proximity to the teleconnection centers, correlations to IOD are generally stronger than to ENSO.Increasing pressure over the Tasman Sea leads to higher (lower) surface temperature over eastern Australia (the southwestern Pacific) in all seasons and is related to reduced surface temperature over Wilkes ...

Mining time-lagged relationships in spatio-temporal climate data

Time series data in climate are often characterized by a delayed relationship between two variables, for example precipitation and temperature anomalies occurring at a place might also occur at another place after some time. These lagged relations generally signify the time lag between the cause and the effect or the spread of a common cause and are important to study and understand as they can aid in prediction. Identifying lagged relationships in climate data is challenging due to the various complex dependencies present in the data like spatial and temporal auto-correlation, seasonality, trends and long distance teleconnections. In this paper, we present a general framework for finding all pairs of lagged positive and negative relations that can exist in a given spatio-temporal dataset. We use a graph based approach based upon the concept of shared reciprocal nearest neighbor to generate cluster pairs of locations sharing similar or opposing behavior for every time lag. Our framework can be generalized to extract multivariate lagged relationships across different variables thus can be used to understand the lagged response of one variable on another. We show the utility of our approach by extracting some of the known delayed relationships like the Madden Julian Oscillation (MJO) and the Pacific North American (PNA) pattern at different lags using the sea level pressure dataset provided by the NCEP/NCAR. Our approach can be broadly applied to other problems in spatio-temporal domain to extract lagged relationships.

Exploring the Predictability of 30-Day Extreme Precipitation Occurrence Using a Global SST–SLP Correlation Network

AbstractCorrelation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation ...

Earth Science Applications of Sensor Data

Advances in earth observation technologies have led to the acquisition of vast volumes of accurate, timely and reliable environmental data which encompass a multitude of information about the land, ocean and atmosphere of the planet. Earth science sensor datasets capture multiple facets of information about natural processes and human activities that shape the physical landscape and environmental quality of our planet, and thus, offer an opportunity to monitor and understand the diverse phenomena affecting earth’s complex system. The monitoring, analysis and understanding of these rich sensor datasets is thus of prime importance for the efficient planning and management of critical resources, since the societal costs of mitigation or adaptation decisions for natural or human-induced adverse events are significant. Hence, a thorough understanding of earth science sensor datasets has a direct impact on a range of societally relevant issues. Moreover, earth science sensor datasets possess unique domain-specific properties that distinguish them from sensor datasets used in other domains, and thus demand the need for novel tools and techniques to be developed for their analysis, adhering to their characteristic issues and challenges.