Evaluation Query Answer over Inconsistent Database with Annotations

2010 ◽  
pp. 36-43
Author(s):  
Aihua Wu
Keyword(s):  
Query Answer ◽  
Inconsistent Database

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

Query Answer over Inconsistent Database with Credible Annotations

2012 ◽  
Vol 23 (5) ◽  
pp. 1167-1182 ◽  
Author(s):  
Ai-Hua WU ◽  
Zi-Jing TAN ◽  
Wei WANG
Keyword(s):  
Query Answer ◽  
Inconsistent Database

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.

scholarly journals Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation

2020 ◽  
Author(s):  
Meghyn Bienvenu ◽  
Camille Bourgaux
Keyword(s):  
Complexity Analysis ◽  
Knowledge Bases ◽  
Data Complexity ◽  
The Core ◽  
Inconsistency Handling ◽  
Definition Of ◽  
Priority Relation ◽  
Inconsistent Database ◽  
The Relationship ◽  
Computational Properties

In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of facts, and a priority relation between conflicting facts. In the database setting, a closely related scenario has been studied and led to the definition of three different notions of optimal repairs (global, Pareto, and completion) of a prioritized inconsistent database. After transferring the notions of globally-, Pareto- and completion-optimal repairs to our setting, we study the data complexity of the core reasoning tasks: query entailment under inconsistency-tolerant semantics based upon optimal repairs, existence of a unique optimal repair, and enumeration of all optimal repairs. Our results provide a nearly complete picture of the data complexity of these tasks for ontologies formulated in common DL-Lite dialects. The second contribution of our work is to clarify the relationship between optimal repairs and different notions of extensions for (set-based) argumentation frameworks. Among our results, we show that Pareto-optimal repairs correspond precisely to stable extensions (and often also to preferred extensions), and we propose a novel semantics for prioritized KBs which is inspired by grounded extensions and enjoys favourable computational properties. Our study also yields some results of independent interest concerning preference-based argumentation frameworks.

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.

Managing Inconsistent Databases Using Active Integrity Constraints

2005 ◽  
pp. 345-350
Author(s):  
Sergio Flesca ◽  
Sergio Greco ◽  
Ester Zumpano
Keyword(s):  
Data Warehousing ◽  
Integrity Constraints ◽  
Database Integration ◽  
Functional Dependencies ◽  
Minimal Set ◽  
Federated Databases ◽  
Inconsistent Databases ◽  
Conflicting Information ◽  
Integration Data ◽  
Inconsistent Database

Integrity constraints are a fundamental part of a database schema. They are generally used to define constraints on data (functional dependencies, inclusion dependencies, exclusion dependencies, etc.), and their enforcement ensures a semantically correct state of a database. As the presence of data inconsistent with respect to integrity constraints is not unusual, its management plays a key role in all the areas in which duplicate or conflicting information is likely to occur, such as database integration, data warehousing, and federated databases (Bry, 1997; Lin, 1996; Subrahmanian, 1994). It is well known that the presence of inconsistent data can be managed by “repairing” the database, that is, by providing consistent databases, obtained by a minimal set of update operations on the inconsistent original environment, or by consistently answering queries posed over the inconsistent database.

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