The paper “ Using Rank Based KNN Queries Processing to Reduce Location Uncertainty in Wireless Sensor Networks” is a forceful variant of term paper on engineering. Imprecise or uncertain data are very common in a number of applications nowadays and this has contributed to the growth of several uncertain database frameworks. Since the k-nearest neighbor (KNN) query is a very important issue in a number of applications, it has been broadly studied. The main aim of this paper is to study the issue of KNN query processing over uncertain data.
To achieve this, I used the expected rank technique to work out KNN. Randomized and exact techniques integrating efficient IO accessing and object pruning techniques were formulated to process queries modeled by uncertain regions or query points. General experiments were carried out on both synthetic and real data to show the efficiency of randomized and exact approaches. Using Rank Based KNN Queries Processing to Reduce Location Uncertainty in Wireless Sensor NetworksIndex Terms— exact technique, expected rank, k-nearest neighbor query, and randomized technique. INTRODUCTIONRapid improvements in positioning technologies, such as wireless communications and Global Positioning System (GPS), have made it possible to track continuously moving objects.
A number of applications may benefit from the growth of such tracking technologies. The advancements of sensors and wireless technology have supported the wide use of sensor systems or networks. Query processing and data collection in a sensor network are challenging research areas in sensor network database management. A number of applications that deal with such data have to come to terms with imprecise or uncertain data. In the vast majority of the evolving applications, the basic datasets are regularly imprecise or uncertain.
There are variations in the sources of uncertainty data among different applications. Privacy preservation, limitations of the devices, delay on data updates, and data incompleteness and randomness are some of the causes of uncertainty data in some applications. Therefore, it is an essential thing for the database community to come up with ways of efficiently handling uncertain data. As the number of applications that employ Spatio-temporal data sets is increasing, it is very important to develop efficient query processing techniques for these applications. In Spatio-temporal databases, the K-Nearest Neighbor (KNN) query is a very important query.
KNN query has been employed in a number of applications including facial pattern recognition, sensor network, traffic network analysis, and location-based services. On the other hand, the data uncertainty in these applications contributes to incorrect results. KNN query over imprecise data is very essential as it is employed in a number of real-life applications. Processing of KNN queries in large Spatio-temporal databases has been extensively examined preciously, but only a few studies have concentrated on uncertain data. To handle the growing needs of providing high-quality services and managing data uncertainty, researchers have suggested the use of “ uncertain databases, ” in which uncertainty is given priority.
Specifically, the uncertain data is assessed through probabilistic queries, which provides solutions with statistical and probabilistic assurance. A data model that is widely used and adopted in databases with uncertainty is the attribute uncertainty, in which the real attribute measure is situated in the uncertainty region or inside a closed region. The probabilistic database in the Attribute-level uncertainty model is an N tuples table. Every tuple has a single attribute with uncertain value.
Each attribute with uncertainty has a discrete pdf defining its distribution. When representing this imprecise association to a given instance, all the tuples draw values for their uncertain attribute in accordance with the related discrete pdf and the selection is not dependent amongst the tuples. A number of studies have described the nearest neighbor (NN) query over data uncertainty as a Probabilistic Nearest Neighbor (PNN) based query, which positions objects with uncertainty according to their probabilities of being a query q nearest neighbor. In other studies, the ranking of uncertain data is based on the U-top and expected score.
The effectiveness of the two semantics is evaluated using five important properties namely: stability, value invariance, unique ranking, containment, and exact-k. The U-top semantic does not satisfy the containment and exact-k properties, while the expected score ranking is responsive to the values and therefore it goes against the property of value invariance. The expected rank suggested meets all the properties that are mentioned above and this motivated me to apply it in this paper to rank objects with uncertainty for KNN queries.
Though I am applying this technique, applying directly as it has been done does not result in effective answers for the KNN queries over imprecise data. This paper will come up with the CPU and IO time-efficient algorithm that will be used to get the expected rank based KNN. The semantic of the KNN query used in this paper varies from the one used in and. The techniques used in and cannot apply in this paper as in, the technique just considers the distance in L1 and in the technique presume that there is a distribution of the distance between the object with uncertainty and a query point.
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