In recent years, a number of indirect data collection methodologies have lead to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, since the corresponding deterministic value is not known. This increases the challenge of mining and managing uncertain data, since the precise behavior of the underlying data is no longer known. In this paper, we provide a survey of uncertain data mining and management applications. In the field of uncertain data management, we will examine traditional methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as classification and clustering. We will also examine a general transform based technique for mining uncertain data. We discuss the models for uncertain data, and how they can be leveraged in a variety of applications. We discuss different methodologies to process and mine uncertain data in a variety of forms.

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A Survey of Uncertain Data Algorithms and Applications

https://www.cise.ufl.edu/~mschneid/Research/thesis_papers/PJD01IS.pdf

A foundation for capturing and querying complex multidimensional data

We extend the OLAP data model to represent data ambiguity, specifically imprecision and uncertainty, and introduce an allocation-based approach to the semantics of aggregation queries over such data. We identify three natural query properties and use them to shed light on alternative query semantics. While there is much work on representing and querying ambiguous data, to our knowledge this is the first paper to handle both imprecision and uncertainty in an OLAP setting.

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OLAP over uncertain and imprecise data

Inrecent years, uncertain datamanagement appli- cations havegrowninimportance because ofthelarge number ofhardwareapplications whichmeasuredataapproximately. Forexample, sensors aretypically expected tohaveconsiderable noise intheir readings because ofinaccuracies indataretrieval, transmission, andpowerfailures. Inmanycases, theestimated error oftheunderlying datastream isavailable. Thisinformation isveryuseful forthemining process, since itcanbeusedinorder toimprove thequality oftheunderlying results. Inthis paper wewill propose amethodforclustering uncertain datastreams. We usea verygeneral modeloftheuncertainty inwhichwe assumethatonlyafewstatistical measures oftheuncertainty are available. We will showthattheuseofevenmodestuncertainty information during theminingprocess issufficient togreatly improve thequality oftheunderlying results. We showthatour approach ismoreeffective thanapurely deterministic method suchastheCluStream approach. We willtesttheapproach ona variety ofrealandsynthetic datasetsandillustrate the advantages ofthemethodintermsofeffectiveness andefficiency.

A Framework forClustering Uncertain Data Streams

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

/pdf/a-framework-for-clustering-uncertain-data-streams-4gpuq1n4ko.pdf

A Framework for Clustering Uncertain Data Streams

Imprecise data in databases were originally denoted as null values, which represent the meaning of "values unknown at present." More generally, a partial value corresponds to a finite set of possible values for an attribute in which exactly one of the values is the "true" value. We define a set of extended aggregate operations, namely sum, average, count, maximum, and minimum, which can be applied to an attribute containing partial values. Two types of aggregate operators are considered: scalar aggregates and aggregate functions. We study the properties of the aggregate operations and develop efficient algorithms for count, maximum and minimum. However, for sum and average, we point out that in general it takes exponential time complexity to do the computations.

F.S.C. Tseng

Papers

Evaluating aggregate operations over imprecise data