Browsing by Author "Kawale, Jaya"
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Item A New Teleconnection : The Australian Southern Oscillation(2012-09-21) Kumar, Arjun; Liess, Stefan; Kawale, Jaya; Ormsby, Dominick; Steinhaeuser, Karsten; Kumar, VipinA possibly new teleconnection has been discovered off the east coast of Australia in the region around Tasman sea and Southern Ocean. Found in pressure anomalies using a novel graph based approach called shared reciprocal nearest neighbors, this dipole appears in reanalysis datasets such as NCEP, JRA, ERA and MERRA. The HadSLP2 observation data shows the new dipole, despite of limited observations in the Tasman Sea. Tests are performed in order to understand the uniqueness of the dipole and its relationship to existing well known phenomena. The dipole index is correlated with known dipole indices such as the SO (Southern Oscillation), AAO (Antarctic Oscillation) with which it shares a marginally higher correlation of less than 0.4 and other northern teleconnections with which it is shown to have a poor relationship. We limit further analysis with only the AAO and SO indices as these are spatially close, have a higher correlation with the new index and tend to influence it in one or more seasons. Seasonal analysis is done to look at the variation in strength as well as its influence on other variables such as TAS (Temperature at Surface), OLR (Outgoing Longwave Radiation), Precipitation etc. We also look at composite maps and do significance tests to determine the significant regions in these maps. We also determine regions that are influenced by the new dipole index alone and are not influenced by other dipoles namely the SO and AAO by looking at difference maps. We discover the dipole at different geopotential heights - 700 hPa, 500 hPa and 50 hPa (Sea Level Pressure is 1013 hPa)- and determine if the dipole is a sea surface phenomenon such as the SO or an upper atmospheric phenomenon such as the AAO. Our tests have shown that we may indeed be looking at a new phenomenon and further tests are being conducted to confirm that.Item Churn Prediction in MMORPGs: A Social Influence Based Approach(2009-05-20) Kawale, Jaya; Pal, Aditya; Srivastava, JaideepMassively Multiplayer Online Role Playing Games (MMORPGs) are computer based games in which players interact with one another in the virtual world. Worldwide revenues for MMORPGs have seen amazing growth in last few years and it is more than a 2 billion dollars industry as per current estimates. Huge amount of revenue potential has attracted several gaming companies to launch online role playing games. One of the major problems these companies suffer apart from ?erce competition is erosion of their customer base. Churn is a big problem for the gaming companies as churners impact negatively in the "word-of-mouth" reports for potential and existing customers leading to further erosion of user base. We study the problem of player churn in the popular MMORPG EverQuest II. The problem of churn prediction has been studied extensively in the past in various domains and social network analysis has recently been applied to the problem to understand the effects of the strength of social ties and the structure and dynamics of a social network in churn. In this paper, we propose a churn prediction model based on examining social in?uence among players and their personal engagement in the game. We hypothesize that social in?uence is a vector quantity, with components negative in?uence and positive in?uence. We propose a modified diffusion model to propagate the in?uence vector in the player's network which represents the social in?uence on the player from his network. We measure a player's personal engagement based on his activity patterns and use it in the modified diffusion model and churn prediction. Our method for churn prediction which combines social in?uence and player engagement factors has shown to improve prediction accuracy significantly for our dataset as compared to prediction using the conventional diffusion model or the player engagement factor, thus validating our hypothesis that combination of both these factors could lead to a more accurate churn prediction.Item Mining relationships in spatio-temporal datasets(2013-01) Kawale, Jaya