Search

Quantities are financial, technological, physical and other measures that denote relevant properties of entities, such as revenue of companies, energy efficiency of cars or distance and brightness of stars and galaxies. Queries with filter conditions on quantities are an important building block for downstream analytics, and pose challenges when the content of interest is spread across a huge number of web tables and other ad-hoc datasets. Search engines support quantity lookups, but largely fail on quantity filters. The QuTE system presented in this paper aims to overcome these problems. It comprises methods for automatically extracting entity-quantity facts from web tables, as well as methods for online query processing, with new techniques for query matching and answer ranking.

Quantities are more than numeric values. They represent measures for entities, expressed in numbers with associated units. Search queries often include quantities, such as athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. Processing such queries requires understanding the quantities, where capturing the surrounding context is an essential part of it. Although modern search engines or QA systems handle entity-centric queries well, they consider numbers and units as simple keywords, and therefore fail to understand the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.) As a result, they cannot generate the correct candidate answers. In this work, we demonstrate a prototype QA system, called Qsearch, that can handle advanced queries with quantity constraints using the common cues present in both query and the text sources.

A novel approach to explore databases using ranked lists is demonstrated. Working with ranked lists, capturing the relative performance of entities, is a very intuitive and widely applicable concept. Users can post lists of entities for which explanatory SQL queries and full result lists are returned. By refining the input, the results, or the queries, user can interactively explore the database content. The demonstrated system is centered around our PALEO framework for reverse engineering OLAP-style database queries and novel work on mining interesting categorical attributes.

In this paper, we propose models, algorithms, and implementation details of an approach that extract the most relevant entity rankings from large datasets. This is done in a fully automated way, as with large amounts of structured data, beyond well understood databases (schemas), manual solutions do not scale. The core task of our approach is to decide which categorical constraints, ranking order (descending or ascending), and length form together an interesting ranking. We make use of a model based on information entropy to find interesting/relevant categorical constraints and devise pruning conditions to avoid generating too many irrelevant rankings. We further investigate the skewness of the value distributions of ranking criteria to find suitable ranking dimensions and ranking order, and present an overall scoring model to assess the meaningfulness of a ranking. For each individual step of our approach, we discuss iterative MapReduce-based algorithms. Finally, the experimental evaluation on real-world data is reported where the users manually evaluate our approach of generating most relevant rankings.

Quantities appear in search queries in numerous forms: companies with annual revenue of at least 50 Mio USD, athletes who ran 200 m faster than 19.5 s, electric cars with range above 400 miles, and so on. Processing such queries requires the understanding of numbers present in the query to capture the contextual information about the queried entities. Modern search engines and QA systems can handle queries that involve entities and types, but they often fail on properly interpreting quantities in queries and candidate answers when the specifics of the search condition (less than, above, etc.), the units of interest (seconds, miles, meters, etc.) and the context of the quantity matter (annual or quarterly revenue, etc.). In this paper, we present a search and QA system, called Qsearch, that can effectively answer advanced queries with quantity conditions. Our solution is based on a deep neural network for extracting quantity-centric tuples from text sources, and a novel matching model to retrieve and rank answers from news articles and other web pages. Experiments demonstrate the effectiveness of Qsearch on benchmark queries collected by crowdsourcing.

Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality., Comment: Updated appendix reporting on Quasimodo v4.3 (2/2021)

Distributed multimedia application over IP network is an evolving paradigm for the researcher in the field of Information and Communication Technology (ICT). There are several applications of this technology —video conferencing, teleteaching and any collaborative application. Maintaining the satisfactory Quality of Service [1] and synchronization is a big challenge to the researchers in this field. In this paper, we extend our previous work [2] by ensur-ing multistream multipoint synchronization as an issue, involving the play out process of two streams (audio and video that were generated at the same time and bear temporal relationship ) at different receivers at the same time and maintaining the temporal relationship between them to achieve fairness among the receivers. In this paper, we propose a distributed algorithm for achieving multi-stream multipoint synchronization and we also model the algorithm with Stochastic Petri Nets and verify its correctness.

Distributed multimedia application over IP network is an evolving paradigm for the researcher in the field of Information and Communication Technology (ICT). There are several applications of this technology—video conferencing, tele-teaching, telemedicine and any collaborative application. Maintaining the satisfactory Quality Of Service [1,2] and synchronization is a big challenge to the researcher in this field. In this paper, we address multipoint synchronization or inter destination synchronization as an issue, involving the play out process of same stream at different receivers at the same time to achieve fairness among the receivers. In this paper, we propose a distributed algorithm for achieving multipoint synchronization and we also model the algorithm with an extension of Petri Nets and verify its correctness.