24,786 research outputs found
Answering Why-not Questions on Reverse Top-k Queries
Why-not questions, which aim to seek clarifications on the missing tuples for query results, have recently received considerable attention from the database community. In this paper, we systematically explore
why-not questions on reverse top-k queries
, owing to its importance in multi-criteria decision making. Given an initial reverse top-
k
query and a missing/why-not weighting vector set
W
m
that is absent from the query result, why-not questions on reverse top-
k
queries explain why
W
m
does not appear in the query result and provide suggestions on how to refine the initial query with minimum penalty to include
W
m
in the refined query result. We first formalize why-not questions on reverse top-
k
queries and reveal their semantics, and then propose a
unified framework
called
WQRTQ
to answer why-not questions on both
monochromatic
and
bichromatic
reverse top-
k
queries. Our framework offers three solutions, namely, (i) modifying a query point
q
, (ii) modifying a why-not weighting vector set
W
m
and a parameter
k
, and (iii) modifying
q
,
W
m
, and
k
simultaneously, to cater for different application scenarios. Extensive experimental evaluation using both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms.
</jats:p
SQL Query Completion for Data Exploration
Within the big data tsunami, relational databases and SQL are still there and
remain mandatory in most of cases for accessing data. On the one hand, SQL is
easy-to-use by non specialists and allows to identify pertinent initial data at
the very beginning of the data exploration process. On the other hand, it is
not always so easy to formulate SQL queries: nowadays, it is more and more
frequent to have several databases available for one application domain, some
of them with hundreds of tables and/or attributes. Identifying the pertinent
conditions to select the desired data, or even identifying relevant attributes
is far from trivial. To make it easier to write SQL queries, we propose the
notion of SQL query completion: given a query, it suggests additional
conditions to be added to its WHERE clause. This completion is semantic, as it
relies on the data from the database, unlike current completion tools that are
mostly syntactic. Since the process can be repeated over and over again --
until the data analyst reaches her data of interest --, SQL query completion
facilitates the exploration of databases. SQL query completion has been
implemented in a SQL editor on top of a database management system. For the
evaluation, two questions need to be studied: first, does the completion speed
up the writing of SQL queries? Second , is the completion easily adopted by
users? A thorough experiment has been conducted on a group of 70 computer
science students divided in two groups (one with the completion and the other
one without) to answer those questions. The results are positive and very
promising
ANSWERING WHY-NOT QUESTIONS ON REVERSE SKYLINE QUERIES OVER INCOMPLETE DATA
Recently, the development of the query-based preferences has received considerable attention from researchers and data users. One of the most popular preference-based queries is the skyline query, which will give a subset of superior records that are not dominated by any other records. As the developed version of skyline queries, a reverse skyline query rise. This query aims to get information about the query points that make a data or record as the part of result of their skyline query. Furthermore, data-oriented IT development requires scientists to be able to process data in all conditions. In the real world, there exist incomplete multidimensional data, both because of damage, loss, and privacy. In order to increase the usability over a data set, this study will discuss one of the problems in processing reverse skyline queries over incomplete data, namely the "why-not" problem. The considered solution to this "why-not" problem is advice and steps so that a query point that does not initially consider an incomplete data, as a result, can later make the record or incomplete data as part of the results. In this study, there will be further discussion about the dominance relationship between incomplete data along with the solution of the problem. Moreover, some performance evaluations are conducted to measure the level of efficiency and effectiveness
Crowdsourcing Multiple Choice Science Questions
We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-specific text and a small set of existing questions. It
produces model suggestions for document selection and answer distractor choice
which aid the human question generation process. With this method we have
assembled SciQ, a dataset of 13.7K multiple choice science exam questions
(Dataset available at http://allenai.org/data.html). We demonstrate that the
method produces in-domain questions by providing an analysis of this new
dataset and by showing that humans cannot distinguish the crowdsourced
questions from original questions. When using SciQ as additional training data
to existing questions, we observe accuracy improvements on real science exams.Comment: accepted for the Workshop on Noisy User-generated Text (W-NUT) 201
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI
- …