Toward Entity-Aware Search

As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability

Text Summarization

With the overwhelming amount of textual information available in electronic formats on the web, there is a need for an efficient text summarizer capable of condensing large bodies of text into shorter versions while keeping the relevant information intact. Such a technology would allow users to get their information in a shortened form, saving valuable time. Since 1997, Microsoft Word has included a summarizer for documents, and currently there are companies that summarize breaking news and send SMS for mobile phones. I wish to create a text summarizer to provide condensed versions of original documents. My focus is on blogs, because people are increasingly using this mode of communication to express their opinions on a variety of topics. Consequently, it will be very useful for a reader to be able to employ a concise summary, tailored to his or her own interests to quickly browse through volumes of opinions relevant to any number of topics. Although many summarization methods exist, my approach involves employing the Lanczos algorithm to compute eigenvalues and eigenvectors of a large sparse matrix and SVD (Singular Value Decomposition) as a means of identifying latent topics hidden in contexts; and the next phase of the process involves taking a high-dimensional set of data and reducing it to a lower-dimensional set. This procedure makes it possible to identify the best approximation of the original text. Since SQL makes it possible to allow analyzing data sets and take advantage of the parallel processing available today, in most database management systems, SQL is employed in my project. The utilization of SQL without external math libraries, however, adds to challenge in the computation of the SVD and the Lanczos algorithm

Performance Analysis of Hadoop MapReduce And Apache Spark for Big Data

In the recent era, information has evolved at an exponential rate. In order to obtain new insights, this information must be carefully interpreted and analyzed. There is, therefore, a need for a system that can process data efficiently all the time. Distributed cloud computing data processing platforms are important tools for data analytics on a large scale. In this area, Apache Hadoop (High-Availability Distributed Object-Oriented Platform) MapReduce has evolved as the standard. The MapReduce job reads, processes its input data and then returns it to Hadoop Distributed Files Systems (HDFS). Although there is limitation to its programming interface, this has led to the development of modern data flow-oriented frameworks known as Apache Spark, which uses Resilient Distributed Datasets (RDDs) to execute data structures in memory. Since RDDs can be stored in the memory, algorithms can iterate very efficiently over its data many times. Cluster computing is a major investment for any organization that chooses to perform Big Data Analysis. The MapReduce and Spark were indeed two famous open-source cluster-computing frameworks for big data analysis. Cluster computing hides the task complexity and low latency with simple user-friendly programming. It improves performance throughput, and backup uptime should the main system fail. Its features include flexibility, task scheduling, higher availability, and faster processing speed. Big Data analytics has become more computer-intensive as data management becomes a big issue for scientific computation. High-Performance Computing is undoubtedly of great importance for big data processing. The main application of this research work is towards the realization of High-Performance Computing (HPC) for Big Data Analysis. This thesis work investigates the processing capability and efficiency of Hadoop MapReduce and Apache Spark using Cloudera Manager (CM). The Cloudera Manager provides end-to-end cluster management for Cloudera Distribution for Apache Hadoop (CDH). The implementation was carried out with Amazon Web Services (AWS). Amazon Web Service is used to configure window Virtual Machine (VM). Four Linux In-stances of free tier eligible t2.micro were launched using Amazon Elastic Compute Cloud (EC2). The Linux Instances were configured into four cluster nodes using Secure Socket Shell (SSH). A Big Data application is generated and injected while both MapReduce and Spark job are run with different queries such as scan, aggregation, two way and three-way join. The time taken for each task to be completed are recorded, observed, and thoroughly analyzed. It was observed that Spark executes job faster than MapReduce

Real Time Web Search Framework for Performing Efficient Retrieval of Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c