Abstract

Sandhi splitting is the primary task for computational processing of text in Sanskrit and Dravidian languages. In these languages, words can join together with morpho-phonemic changes at the point of joining. This phenomenon is known as Sandhi. Sandhi splitter splits the string of conjoined words into individual words. Accurate execution of sandhi splitting is crucial for text processing tasks such as POS tagging, topic modelling and document indexing. We have tried different approaches to address the challenges of sandhi splitting in Malayalam, and finally, we have thought of exploiting the phonological changes that take place in the words while joining. This resulted in a hybrid method which statistically identifies the split points and splits using predefined character level linguistic rules. Currently, our system gives an accuracy of 91.1%.

Abstract

With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21% in terms of energy consumption and in terms of maintaining the SLAs, it performs 60% better than Single Threshold algorithm.

Abstract

As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema. We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.

Abstract

Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean — content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party Web pages, but the ads in this form are rich multimedia ads — image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user's probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser's side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.

Abstract

Current PaaS solutions use an underlying IaaS provider and restrict themselves to a few geographical regions. They operate in one/two zones only and use others for failover or disaster recovery. In this paper, we present the design of MultiPaaS, a PaaS solution which runs on multiple cloud/IaaS providers and leverages the combined global span. Unlike traditional scaling techniques where app servers are scaled up or down in a specific region or data center, we present a design to perform it on a global scale. Origin of the request burst is identified and instances are booted in a data center closest to it. It ensures database persistence and cross-region scalability via a combination of replication techniques. We identify common features available across all IaaS providers and use that as a provider interface to ensure interoperability, but use provider specific extensions to leverage unique offerings and benefits.

Abstract

Our computing demands have grown so much that we need a robust distributed computing platform to process data. To feed such data hungry systems, we need an equally robust distributed file systems that span across multiple geographically separate locations. Most distributed file systems break any file into a set of blocks or chunks which are spread across the cluster. The major bottleneck is to identify where to place the blocks and where to place the replicas so that cluster is optimized on certain parameters like disk utilization, minimizing network congestion, maximizing throughput, optimal power utilization, etc. This paper proposes assigning a distance measure for each one of the data sources with respect to the other and placing the blocks on the specified disk on the node that minimizes the total distance of the last few requests made for that block. As the request pattern and parameters change, the distances are updated and the blocks are moved dynamically to minimized the distance, in effect optimizing on the required parameters. The distance function is to be modeled based on the cluster and the parameters you wish to optimize on. It can be a function of just bandwidth or bandwidth and latency or other miscellaneous features like disk utilization, processing power, disk speed, power utilization, cooling requirements, temperature, etc. A detailed performance analysis was carried out with disk bandwidth, network bandwidth and disk utilization as the parameters and the performance is far better in comparison to the reference system (10% or more depending on specification differences) which has no understanding of the different type of disks present and the nature of the cluster. Due to the performance aware nature, the system was inherently able to utilize memory for speeding up performance through the use of in-memory partitions.

Abstract

Given a knowledge base, annotating any text with entities in the knowledge base enhances automated understanding of the text. Entities provide extra contextual information for the automated system to understand and interpret the text better. In the special case when the text is in the form of short text queries, automated understanding can be critical in improving the quality of search results and recommendations. Annotation of queries helps semantic retrieval, ensuring diversity of search results including retrieval of relevant news stories. In this paper, we present SIEL@ERD, a system for automated stamping of entity information in short query text. Our system builds from the state-of-the-art TAGME system and is optimized for time and performance efficiency. Our system achieved an F1 measure of 0.53 and the latency of 0.31 seconds on a dataset of 500 queries and a Freebase snapshot provided for the short track in the Entity Recognition and Disambiguation Challenge at SIGIR 2014.

Abstract

Entities are centric to a large number of real world applications. Wikipedia shows entity infoboxes for a large number of entities. However, not much structured information is available about character entities in books. Automatic discovery of characters from books can help in effective summarization. Such a structured summary which not just introduces characters in the book but also provides a high level relationship between them can be of critical importance for buyers. This task involves the following challenging novel problems: 1. automatic discovery of important characters given a book; 2. automatic social graph construction relating the discovered characters; 3. automatic summarization of text most related to each of the characters; and 4. automatic infobox extraction from such summarized text for each character. As part of this demo, we design mechanisms to address these challenges and experiment with publicly available books.

Abstract

A large number of web queries are related to product entities. Studying evolution of product entities can help analysts understand the change in particular attribute values for these products. However, studying the evolution of a product requires us to be able to link various versions of a product together in a temporal order. While it is easy to temporally link recent versions of products in a few domains manually, solving the problem in general is challenging. The ability to temporally order and link various versions of a single product can also improve product search engines. In this paper, we tackle the problem of finding the previous version (predecessor) of a product entity. Given a repository of product entities, we first parse the product names using a CRF model. After identifying entities corresponding to a single product, we solve the problem of finding the previous version of any given particular version of the product. For the second task, we leverage innovative features with a Naïve Bayes classifier. Our methods achieve a precision of 88% in identifying the product version from product entity names, and a precision of 53% in identifying the predecessor.

Abstract

For privacy reasons, personally identifiable information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the fields of advertising, forensics and business intelligence. Existing methods for this problem have focused on classifier learning using content based features like word n-grams and style based features like Part of Speech (POS) n-grams. Two major drawbacks of previous approaches are:(1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. We propose a novel method to address these drawbacks by representing the document using Wikipedia concepts and category information. Experimental results show that classifiers learned using such features along with previously used features help us achieve significantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features.