Sampling Techniques for Big Data Analysis

Determination of Optimal Sampling Design for Spatial Data Analysis

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]

Algorithmic Techniques for Big Data

Handling an Update: When an update (it, ct) arrives, then ct is added to one entry in each row of the array count. Specifically, ∀1 ≤ j ≤ d, count [j, hj(it)]← count [j, hj(it)] + ct. Lemma 1. The space used by Count-Min Sketch is O(wd) ≡ O( ln 1 δ ) words. Specifically, it uses an array which takes wd words and d hash functions, each of which can be stored using 2 words. An update can be handl...

Various Data-Mining Techniques for Big Data

Big data is the word used to describe structured and unstructured data. The term big data is originated from the web search companies who had to query loosely structured very large

Data Interpolation: An Efficient Sampling Alternative for Big Data Aggregation

Given a large set of measurement sensor data, in order to identify a simple function that captures the essence of the data gathered by the sensors, we suggest representing the data by (spatial) functions, in particular by polynomials. Given a (sampled) set of values, we interpolate the datapoints to define a polynomial that would represent the data. The interpolation is challenging, since in pr...

Probabilistic Hashing Techniques for Big Data