Spatial database management system (SDBMS) contains spatial data in space and provides special storage for handling the spatial data. With the perception of users attraction towards few best objects rather than a large list of best objects as a result, in this paper, we propose an approach to generate top k ranking of spatial data for selected location based on quality of features. Search Algor...

This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a Gaussian probability density function. Since the Naive approach is very expensive due to costly distance function between uncertain objects, a populated-cell list (PC-list) based top-k distance-based outlier detection approach is proposed in this work. W...

Given a query location and a set of query keywords, a top-k spatial keyword query rank objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top-k spatial keyword queries in Euclidean space. However, few algorithms study top-k keyword queries in undirected road networks where every road segment is undirected. Ev...

1. Summary. The paper proposes a novel streaming algorithm to mine the top-k episodes in a stream of events. The frequency of the episodes is computed over a sliding window which length is defined by the user. The key idea in this paper is based on two new concepts related to the stream: maximum rate of change and top-k separation. The sliding window is decomposed into batches and the previous ...

Multiple sensors and sensor fusion are commonly used to get more accurate information. The intuitive method to store multi-sensory data is using uncertain database because the sensors are not precise enough. Hence, like the top-k queries in traditional database, the top-k queries in uncertain databases are quite popular and useful due to its wide application. Although there are lots of top-k qu...

Information systems apply various techniques to rank query answers. Ranking queries (or top-k queries) are dominant in many emerging applications, e.g., similarity queries in multimedia databases, searching web databases, midlewares and data mining. In such application domains, end-users are more interested in the most important (top-k) query answers in the potentially huge answer space. Thus f...

An important subject in integration of information in the large scale is to select Topic with a view to ranking from multiple sources so that transfer cost is become minimum. For this purpose in relations join, the suitable size of relations inputs for getting Top K must be determined. We are presenting in this article, according to the quantity k that is determined in query, a dynamic algorith...

Regret minimizing sets are a very recent approach to representing a dataset D with a small subset S of representative tuples. The set S is chosen such that executing any top-1 query on S rather than D is minimally perceptible to any user. To discover an optimal regret minimizing set of a predetermined cardinality is conjectured to be a hard problem. In this paper, we generalize the problem to t...

identifying clusters or clustering is an important aspect of data analysis. it is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. it is a main task of exploratory data mining, and a common technique for statistical data analysis this paper proposed an improved version of k-means algorithm, namely persistent k...