System for Cooperative Query Answer Authentication in Cloud over Anonymous Data

2018 ◽  
Author(s):  
Shashikant S. Nagdive ◽  
Prashant N. Chatur
Keyword(s):  
Query Answer

Technical Perspective for

2021 ◽  
Vol 50 (1) ◽  
pp. 77-77
Author(s):  
Dan Suciu
Keyword(s):  
Online Monitoring ◽  
Data Analyst ◽  
Query Answer ◽  
Why Questions

When a data analyst runs some query to analyze her data, she often wants to ask some follow-up questions, about the result of the query. Why-questions take many shapes, and occur in many scenarios. Why is a particular tuple in the answer? Why is it not in the answer? Why is this graph decreasing? Why did we observe a sudden burst of error messages in online monitoring? Database researchers have noted the need for why-questions, and the literature contains several approaches, mostly tailored to specific applications. Despite the interest and the work in this area, there is currently no consensus of what an explanation to a query answer should be, and how one should compute it.

Sensors, Uncertainty Models, and Probabilistic Queries

2005 ◽  
pp. 613-618 ◽  
Author(s):  
Reynold Cheng ◽  
Sunil Prabhakar
Keyword(s):  
Time Instant ◽  
Network Bandwidth ◽  
User Query ◽  
Data Value ◽  
Query Answer ◽  
Fire Alarm System ◽  
Probabilistic Queries ◽  
The Status ◽  
Recorded Data ◽  
Uncertainty Models

Sensors are often used to monitor the status of an environment continuously. The sensor readings are reported to the application for making decisions and answering user queries. For example, a fire alarm system in a building employs temperature sensors to detect any abrupt change in temperature. An aircraft is equipped with sensors to track wind speed, and radars are used to report the aircraft’s location to a military application. These applications usually include a database or server to which the sensor readings are sent. Limited network bandwidth and battery power imply that it is often not practical for the server to record the exact status of an entity it monitors at every time instant. In particular, if the value of an entity (e.g., temperature, location) monitored is constantly evolving, the recorded data value may differ from the actual value. Querying the database can then produce incorrect results. Consider a simple example where a user asks the database: “Which room has a temperature between 10oF and 20oF?” If we represent temperature values of rooms A and B stored in the database by A0 and B0 respectively, we can see from Figure 1(a) that the answer to the user query is “Room B”. In reality, the temperature values of both rooms may have changed to newer values, A1 and B1, as shown in Figure 1(b), where the true query answer should be “Room A”. Unfortunately, because of transmission delay, these newest pieces of information are not propagated in time to the system to supply fresh data to the query, and consequently the query is unable to yield a correct answer.

A Safe-Region Approach to a Moving k-RNN Queries in a Directed Road Network

2017 ◽  
Vol 26 (05) ◽  
pp. 1750071 ◽  
Author(s):  
Kamil Zeberga ◽  
Rize Jin ◽  
Hyung-Ju Cho ◽  
Tae-Sun Chung
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Exact Formula ◽  
Specific Situation ◽  
The Road ◽  
Query Answer ◽  
Safe Region ◽  
On The Road ◽  
Conventional Solution ◽  
Road Segments

In road networks, [Formula: see text]-range nearest neighbor ([Formula: see text]-RNN) queries locate the [Formula: see text]-closest neighbors for every point on the road segments, within a given query region defined by the user, based on the network distance. This is an important task because the user's location information may be inaccurate; furthermore, users may be unwilling to reveal their exact location for privacy reasons. Therefore, under this type of specific situation, the server returns candidate objects for every point on the road segments and the client evaluates and chooses exact [Formula: see text] nearest objects from the candidate objects. Evaluating the query results at each timestamp to keep the freshness of the query answer, while the query object is moving, will create significant computation burden for the client. We therefore propose an efficient approach called a safe-region-based approach (SRA) for computing a safe segment region and the safe exit points of a moving nearest neighbor (NN) query in a road network. SRA avoids evaluation of candidate answers returned by the location-based server since it will have high computation cost in the query side. Additionally, we applied SRA for a directed road network, where each road network has a particular orientation and the network distances are not symmetric. Our experimental results demonstrate that SRA significantly outperforms a conventional solution in terms of both computational and communication costs.

Query Answer Authentication

2012 ◽  
Vol 4 (2) ◽  
pp. 1-103 ◽  
Author(s):  
HweeHwa Pang ◽  
Kian-Lee Tan
Keyword(s):  
Query Answer

Toward a Framework for Advanced Query Processing

2001 ◽  
pp. 317-330
Author(s):  
Suk-Chung Yoon
Keyword(s):  
Data Mining ◽  
Computational Complexity ◽  
Query Processing ◽  
Final Section ◽  
Future Research ◽  
Generation Process ◽  
Data Mining Techniques ◽  
Query Answer ◽  
Different Types ◽  
Using Data

The contribution of our approach is that we develop a framework for processing and answering queries flexibly by applying data mining techniques. In addition, we suggest strategies to reduce the computational complexity of the advanced query answer generation process. We believe that our approach enhances user-machine interfaces significantly to conventional databases with additional features. This chapter is structured as follows. The next section introduces motivating examples to show the advantages of advanced query processing. Following that we survey related works on intelligent query processing. Then we present our approach to process different types of queries using data mining techniques. The final section discusses our conclusions and possible extensions of our work for future research.

Annotation Based Query Answer over Inconsistent Database

2010 ◽  
Vol 25 (3) ◽  
pp. 469-481 ◽  
Author(s):  
Ai-Hua Wu ◽  
Zi-Jing Tan ◽  
Wei Wang
Keyword(s):  
Query Answer ◽  
Inconsistent Database

scholarly journals Improving the Quality of the User Experience by Query Answer Modification

2020 ◽  
Author(s):  
João Pedro Valladão Pinheiro ◽  
Marco Antonio Casanova ◽  
Elisa Souza Menendez
Keyword(s):  
User Experience ◽  
Query Answer

This paper proposes a process that modifies the presentation of a query answer to improve the quality of the user’s experience. The process is particularly useful when the answer is long and repetitive. The process reorganizes the original query answer by applying heuristics to summarize the results and to select template questions that create a user dialog that guides the presentation of the results.

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