IJSRST162672 | Received : 03 Dec 2016 | Accepted : 12 Dec 2016 | November-December-2016 [(2)6 : 359-361] © 2016 IJSRST | Volume 2 | Issue 6 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Engineering and Technology 359 Top-K Dominating Queries On Incomplete Data : A Survey Jilu Sajeev, Noorjahan V. A. Department of Computer Science and Engineering, Ilahia College of Engineering & Technology, Muvattupuzha, India ABSTRACT Top-k dominating queries output the k objects that are dominating all other objects in a dataset. In most of the existing systems the dataset is assumed as complete. But in practical examples the dataset may be incomplete due to various reasons. In this paper a survey on various methods used to find the dominating objects from an incomplete dataset. Keywords : Top-K Query, Dominance Relation, Skyline, Bucketing I. INTRODUCTION Top-k dominating queries combine the advantages of top-k queries and skyline queries. There are many works based on top-k dominating queries on complete data. But in real-time applications it is not necessary that the datasets are complete. The incompleteness means that some dimensions in the dataset are missing. The reasons for incomplete dataset may be dataloss, privacy preservation and so on. For example, consider the object A from a dataset. The dimensions of A is (1, 7, -, 4).There is 4 dimensions for the object given and the dimension „–„ indicates a missing value. When using this type of dataset it is difficult to find the top-k objects because some dimensions are missing so that they are incomparable with others. So it is important that how to find dominating elements from the incomplete dataset. To output the dominating objects from a dataset first of all we need to define the dominance relationship in an incomplete dataset. Definition :( dominance relationship on incomplete data [1]). Given two objects o and o’ in a dataset S. o dominates o’ (i.e., o < o’) if the following conditions hold: I) for every dimension i,either o. [i] is less than o’.[i] or at least one of them is missing. II) there is at least one dimension j in which both o. [j] and o’. [j] are observed and o.[j] is less than o’.[j]. Consider an incomplete dataset given in fig 1, in which 4 objects are given with 5 dimensions for each object. In object A1 third dimension value is missing and also in all other objects we can see that some dimensions values are not available. While checking the dominance relationship between objects by the above definition first we need to compare A1 with A2.For each dimensions available in both A1 and A2, A2 dominates A1 so score of A2 becomes 1. In this way comparing each objects with others we can find the score of the entire dataset elements. Figure 1. A Sample Dataset But in case of large dataset it is not possible to compare each elements become complex and time consuming. So there may be simple and speedy methods to find the dominant elements. This paper explains some previous works done on this subject. II. METHODS AND MATERIAL Related Works This section includes some details about previous works related to Top-K dominating queries on incomplete data.