Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data

Inverted Index Entry Invalidation Strategy for Real Time Search

The impressive rise of user-generated content on the web in the hands of sites like Twitter imposes new challenges to search systems. The concept of real-time search emerges, increasing the role that efficient indexing and retrieval algorithms play in this scenario. Thousands of new updates need to be processed in the very moment they are generated and users expect content to be “searchable” within seconds. This lead to the develop of efficient data structures and algorithms that may face this challenge efficiently. In this work, we introduce the concept of index entry invalidator, a strategy responsible for keeping track of the evolu- tion of the underlying vocabulary and selectively invalidóte and evict those inverted index entries that do not considerably degrade retrieval effectiveness. Consequently, the index becomes smaller and may increase overall efficiency. We study the dynamics of the vocabulary using a real dataset and also provide an evaluation of the proposed strategy using a search engine specifically designed for real-time indexing and search.XII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Improving Real Time Search Performance using Inverted Index Entries Invalidation Strategies

The impressive rise of user-generated content on the web in the hands of sites like Twitter imposes new challenges to search systems. The concept of real-time search emerges, increasing the role that efficient indexing and retrieval algorithms play in this scenario. Thousands of new updates need to be processed in the very moment they are generated and users expect content to be “searchable” within seconds. This lead to the develop of efficient data structures and algorithms that may face this challenge efficiently. In this work, we introduce the concept of index entry invalidator, a strategy responsible for keeping track of the evolution of the underlying vocabulary and selectively invalidate and evict those inverted index entries that do not considerably degrade retrieval effectiveness. Consequently, the index becomes smaller and may increase overall efficiency. We introduce and evaluate two approaches based on Time-to-Live and Sliding Windows criteria. We also study the dynamics of the vocabulary using a real dataset while the evaluation is carry out using a search engine specifically designed for real-time indexing and search.Facultad de Informátic

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

An ant-inspired, deniable routing approach in ad hoc question & answer networks

The ubiquity of the Internet facilitates electronic question and answering (Q&A) between real people with ease via community portals and social networking websites. It is a useful service which allows users to appeal to a broad range of answerers. In most cases however, Q&A services produce answers by presenting questions to the general public or associated digital community with little regard for the amount of time users spend examining and answering them. Ultimately, a question may receive large amounts of attention but still not be answered adequately. Several existing pieces of research investigate the reasons why questions do not receive answers on Q&A services and suggest that it may be associated with users being afraid of expressing themselves. Q&A works well for solving information needs, however, it rarely takes into account the privacy requirements of the users who form the service. This thesis was motivated by the need for a more targeted approach towards Q&A by distributing the service across ad hoc networks. The main contribution of this thesis is a novel routing technique and networking environment (distributed Q&A) which balances answer quality and user attention while protecting privacy through plausible deniability. Routing approaches are evaluated experimentally by statistics gained from peer-to-peer network simulations, composed of Q&A users modelled via features extracted from the analysis of a large Yahoo! Answers dataset. Suggestions for future directions to this work are presented from the knowledge gained from our results and conclusion

Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks

Textual data and analysis can derive new insights and bring valuable business insights. These insights can be further leveraged by making better future business decisions. Sources that are used for text analysis in financial industry vary from internal word documents, email to external sources like social media, websites or open data. The system described in this paper will utilize data from social media (Twitter) and tweets related to Italian banks, in Italian. This system is based on open source tools (R language) and topic extraction model was created to gather valuable information. This paper describes methods used for data ingestion, modelling, visualizations of results and insights. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Temporal Feedback for Tweet Search with Non-Parametric Density Estimation

This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feedback: starting with an initial set of results from a baseline retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Experiments on TREC datasets confirm that our temporal feedback formulation improves search effectiveness, thus providing support for our hypothesis. Our approach outperforms both a standard baseline and previous temporal retrieval models. Temporal feedback improves over standard lexical feedback (with and without human judgments), illustrating that temporal relevance signals exist independently of document content