Deepak P

CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices

Mobile-enabled social networks applications are becoming increasingly popular. Most of the current social network applications have been designed for high-end mobile devices, and they rely upon features such as GPS, capabilities of the world wide web, and rich media support. However, a significant fraction of mobile user base, especially in the developing world, own low-end devices that are only capable of voice and short text messages (SMS). In this context, a natural question is whether one can design meaningful social network-based applications that can work well with these simple devices, and if so, what the real challenges are. Towards answering these questions, this paper presents a social network-based recommender system that has been explicitly designed to work even with devices that just support phone calls and SMS. Our design of the social network based recommender system incorporates three features that complement each other to derive highly targeted ads. First, we analyze information such as customers address books to estimate the level of social affinity among various users. This social affinity information is used to identify the recommendations to be sent to an individual user. Second, we combine the social affinity information with the spatio-temporal context of users and historical responses of the user to further refine the set of recommendations and to decide when a recommendation would be sent. Third, social affinity computation and spatio-temporal contextual association are continuously tuned through user feedback. We outline the challenges in building such a system, and outline approaches to deal with such challenges.

Efficient reverse skyline retrieval with arbitrary non-metric similarity measures

A Reverse Skyline query returns all objects whose skyline contains the query object. In this paper, we consider Reverse Skyline query processing where the distance between attribute values are not necessarily metric. We outline real world cases that motivate Reverse Skyline processing in such scenarios. We consider various optimizations to develop efficient algorithms for Reverse Skyline processing. Firstly, we consider block-based processing of objects to optimize on IO costs. We then explore pre-processing to re-arrange objects on disk to speed-up computational and IO costs. We then present our main contribution, which is a method of using group-level reasoning and early pruning to micro-optimize processing by reducing attribute level comparisons. An extensive empirical evaluation with real-world datasets and synthetic data of varying characteristics shows that our optimization techniques are indeed very effective in dramatically speeding Reverse Skyline processing, both in terms of computational costs and IO costs.

Indexing and matching trajectories under inconsistent sampling rates

Quantifying the similarity between two trajectories is a fundamental operation in analysis of spatio-temporal databases. While a number of distance functions exist, the recent shift in the dynamics of the trajectory generation procedure violates one of their core assumptions; a consistent and uniform sampling rate. In this paper, we formulate a robust distance function called Edit Distance with Projections (EDwP) to match trajectories under inconsistent and variable sampling rates through dynamic interpolation. This is achieved by deploying the idea of projections that goes beyond matching only the sampled points while aligning trajectories. To enable efficient trajectory retrievals using EDwP, we design an index structure called TrajTree. TrajTree derives its pruning power by employing the unique combination of bounding boxes with Lipschitz embedding. Extensive experiments on real trajectory databases demonstrate EDwP to be up to 5 times more accurate than the state-of-the-art distance functions. Additionally, TrajTree increases the efficiency of trajectory retrievals by up to an order of magnitude over existing techniques.

Efficient online top-K retrieval with arbitrary similarity measures

The top-k retrieval problem requires finding k objects most similar to a given query object. Similarities between objects are most often computed as aggregated similarities of their attribute values. We consider the case where the similarities between attribute values are arbitrary (non-metric), due to which standard space partitioning indexes cannot be used. Among the most popular techniques that can handle arbitrary similarity measures is the family of threshold algorithms. These were designed as middleware algorithms that assume that similarity lists for each attribute are available and focus on efficiently merging these lists to arrive at the results. In this paper, we explore multi-dimensional indexing of non-metric spaces that can lead to efficient pruning of the search space utilizing inter-attribute relationships, during top-k computation. We propose an indexing structure, the AL-Tree and an algorithm to do top-k retrieval using it in an online fashion. The ALTree exploits the fact that many real world attributes come from a small value space. We show that our algorithm performs much better than the threshold based algorithms in terms of computational cost due to efficient pruning of the search space. Further, it out-performs them in terms of IOs by upto an order of magnitude in case of dense datasets.

SymAB: symbol-based address book for the semi-literate mobile user

Developing countries like India are observing an increasing trend in the penetration of mobile phones towards the base of the pyramid (lower strata of the society). This segment comprises of users who are novice and semiliterate and are interested in the basic usage of the mobile phone. This paper explores one of the basic features, the address book for its usability and presents an enhanced symbol-based design to cater for the semi-literate user. The enhancement uses symbols to replace current text based storage and retrieval and also includes a call distribution based address book access to align with the skewed nature of the users requirements. The results of a preliminary evaluation of the prototype are encouraging regarding the value perceived through the design.

Retrieving similar discussion forum threads: a structure based approach

Online forums are becoming a popular way of finding useful information on the web. Search over forums for existing discussion threads so far is limited to keyword-based search due to the minimal effort required on part of the users. However, it is often not possible to capture all the relevant context in a complex query using a small number of keywords. Example-based search that retrieves similar discussion threads given one exemplary thread is an alternate approach that can help the user provide richer context and vastly improve forum search results. In this paper, we address the problem of finding similar threads to a given thread. Towards this, we propose a novel methodology to estimate similarity between discussion threads. Our method exploits the thread structure to decompose threads in to set of weighted overlapping components. It then estimates pairwise thread similarities by quantifying how well the information in the threads are mutually contained within each other using lexical similarities between their underlying components. We compare our proposed methods on real datasets against state-of-the-art thread retrieval mechanisms wherein we illustrate that our techniques outperform others by large margins on popular retrieval evaluation measures such as NDCG, MAP, Precision@k and MRR. In particular, consistent improvements of up to 10% are observed on all evaluation measures.

Exploiting evidence from unstructured data to enhance master data management

Master data management (MDM) integrates data from multiple structured data sources and builds a consolidated 360-degree view of business entities such as customers and products. Todays MDM systems are not prepared to integrate information from unstructured data sources, such as news reports, emails, call-center transcripts, and chat logs. However, those unstructured data sources may contain valuable information about the same entities known to MDM from the structured data sources. Integrating information from unstructured data into MDM is challenging as textual references to existing MDM entities are often incomplete and imprecise and the additional entity information extracted from text should not impact the trustworthiness of MDM data. In this paper, we present an architecture for making MDM text-aware and showcase its implementation as IBM Info-Sphere MDM Extension for Unstructured Text Correlation, an add-on to IBM InfoSphere Master Data Management Standard Edition. We highlight how MDM benefits from additional evidence found in documents when doing entity resolution and relationship discovery. We experimentally demonstrate the feasibility of integrating information from unstructured data sources into MDM.

Generating a Word-Emotion Lexicon from #Emotional Tweets

Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classification performance.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as—weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases—(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Operators for Similarity Search: Semantics, Techniques and Usage Scenarios

This book provides a comprehensive tutorial on similarity operators. The authors systematically survey the set of similarity operators, primarily focusing on their semantics, while also touching upon mechanisms for processing them effectively. The book starts off by providing introductory material on similarity search systems, highlighting the central role of similarity operators in such systems. This is followed by a systematic categorized overview of the variety of similarity operators that have been proposed in literature over the last two decades, including advanced operators such as RkNN, Reverse k-Ranks, Skyline k-Groups and K-N-Match. Since indexing is a core technology in the practical implementation of similarity operators, various indexing mechanisms are summarized. Finally, current research challenges are outlined, so as to enable interested readers to identify potential directions for future investigations. In summary, this book offers a comprehensive overview of the field of similarity search operators, allowing readers to understand the area of similarity operators as it stands today, and in addition providing them with the background needed to understand recent novel approaches.