1,101 research outputs found
ï»żAn Answer Explanation Model for Probabilistic Database Queries
Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. \ud
The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study
Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)
This paper introduces a scalable approach for probabilistic top-k similarity
ranking on uncertain vector data. Each uncertain object is represented by a set
of vector instances that are assumed to be mutually-exclusive. The objective is
to rank the uncertain data according to their distance to a reference object.
We propose a framework that incrementally computes for each object instance and
ranking position, the probability of the object falling at that ranking
position. The resulting rank probability distribution can serve as input for
several state-of-the-art probabilistic ranking models. Existing approaches
compute this probability distribution by applying a dynamic programming
approach of quadratic complexity. In this paper we theoretically as well as
experimentally show that our framework reduces this to a linear-time complexity
while having the same memory requirements, facilitated by incremental accessing
of the uncertain vector instances in increasing order of their distance to the
reference object. Furthermore, we show how the output of our method can be used
to apply probabilistic top-k ranking for the objects, according to different
state-of-the-art definitions. We conduct an experimental evaluation on
synthetic and real data, which demonstrates the efficiency of our approach
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM
SIGMOD conference 201
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