Program booklet in PDF
... Principles of Database Systems (PODS 2011), held in Athens, Greece, on June 13 – 15, 2011, in conjunction with the 2011 ACM SIGMOD International Conference on Management of Data. The proceedings include a paper by Daniel Deutch and Tova Milo based on the keynote address by Tova Milo and two papers, ...
... Principles of Database Systems (PODS 2011), held in Athens, Greece, on June 13 – 15, 2011, in conjunction with the 2011 ACM SIGMOD International Conference on Management of Data. The proceedings include a paper by Daniel Deutch and Tova Milo based on the keynote address by Tova Milo and two papers, ...
ORIGINAL ARTICLES Mining Sequential Pattern With Synchronous
... for every event. By probing the difference of time lists, we develop two mining approaches for mining periodic sections of distinct events. Potential Cycle Detection (PCD): A suitable pattern with time l valid entails that there subsists as a minimum min rep matches. Consequently, we first employ an ...
... for every event. By probing the difference of time lists, we develop two mining approaches for mining periodic sections of distinct events. Potential Cycle Detection (PCD): A suitable pattern with time l valid entails that there subsists as a minimum min rep matches. Consequently, we first employ an ...
Horizontal Aggregations in SQL to Prepare Data Sets for Data
... a parameter associated to the aggregation itself. That is why they appear inside the parenthesis as arguments, but alternative syntax definitions are feasible. In the context of our work, H() represents some SQL aggregation (e.g. sum(), count(), min(), max(), avg()). The function H() must have at lea ...
... a parameter associated to the aggregation itself. That is why they appear inside the parenthesis as arguments, but alternative syntax definitions are feasible. In the context of our work, H() represents some SQL aggregation (e.g. sum(), count(), min(), max(), avg()). The function H() must have at lea ...
Abnormal Pattern Recognition in Spatial Data
... Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on “global comparison” and identifies deviations from the remainder of the entire data set. In c ...
... Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on “global comparison” and identifies deviations from the remainder of the entire data set. In c ...
Distributed and Stream Data Mining Algorithms for
... patterns are those included in a user-specified number of input set. The second one consists in finding frequent sequential patterns in a database of time-stamped events. Since the proposed framework uses (exact) frequent pattern mining algorithms as the building block of the approximate distributed ...
... patterns are those included in a user-specified number of input set. The second one consists in finding frequent sequential patterns in a database of time-stamped events. Since the proposed framework uses (exact) frequent pattern mining algorithms as the building block of the approximate distributed ...
Multidimensional Analysis and Descriptive Mining of
... spatial operations, such as spatial union or spatial clustering methods. Aggregation and approximation are important techniques for this form of generalization. Other spatial ...
... spatial operations, such as spatial union or spatial clustering methods. Aggregation and approximation are important techniques for this form of generalization. Other spatial ...
REAFUM: Representative Approximate Frequent Subgraph Mining
... different graphs. For example, a protein may have accumulated a few mutations through evolution which may lead to small structural variations but do not alter the protein’s function [1]. Graph mining approaches based on exact matching will miss these patterns when all the corresponding variations ar ...
... different graphs. For example, a protein may have accumulated a few mutations through evolution which may lead to small structural variations but do not alter the protein’s function [1]. Graph mining approaches based on exact matching will miss these patterns when all the corresponding variations ar ...
Data Mining with an Ant Colony Optimization Algorithm
... In this paper we are interested in a particular behavior of real ants, namely the fact that they are capable of finding the shortest path between a food source and the nest (adapting to changes in the environment) without the use of visual information [9]. This intriguing ability of almost-blind ant ...
... In this paper we are interested in a particular behavior of real ants, namely the fact that they are capable of finding the shortest path between a food source and the nest (adapting to changes in the environment) without the use of visual information [9]. This intriguing ability of almost-blind ant ...
Mining Sensor Data in Smart Environment for
... Allen suggested that it was more common to describe scenarios by time intervals rather than by time points, and listed thirteen relations comprising a temporal logic: before, after, meets, meetby, overlaps, overlapped-by, starts, started-by, finishes, finishedby, during, contains, equals (displayed ...
... Allen suggested that it was more common to describe scenarios by time intervals rather than by time points, and listed thirteen relations comprising a temporal logic: before, after, meets, meetby, overlaps, overlapped-by, starts, started-by, finishes, finishedby, during, contains, equals (displayed ...
New Method to Improve Mining of Multi
... Marwa Fouad Al-Rouby Abstract Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a m ...
... Marwa Fouad Al-Rouby Abstract Class imbalance is one of the challenging problems for data mining and machine learning techniques. The data in real-world applications often has imbalanced class distribution. That is occur when most examples are belong to a majority class and few example belong to a m ...
Oracle Data Mining Concepts
... The Programs (which include both the software and documentation) contain proprietary information; they are provided under a license agreement containing restrictions on use and disclosure and are also protected by copyright, patent, and other intellectual and industrial property laws. Reverse engine ...
... The Programs (which include both the software and documentation) contain proprietary information; they are provided under a license agreement containing restrictions on use and disclosure and are also protected by copyright, patent, and other intellectual and industrial property laws. Reverse engine ...
Compact Transaction Database for Efficient Frequent Pattern Mining
... were generated using the procedure described in [4]. These transactions mimic the actual transactions in a retail environment. The transaction generator takes the parameters shown in Table III. Each synthetic data set is named after these parameters. For example, the data set T10.I5.D20K uses the pa ...
... were generated using the procedure described in [4]. These transactions mimic the actual transactions in a retail environment. The transaction generator takes the parameters shown in Table III. Each synthetic data set is named after these parameters. For example, the data set T10.I5.D20K uses the pa ...
A comprehensive review on privacy preserving data
... over the entire data (Aggarwal and Yu 2008). Despite much research a method with satisfactory privacy settings are far from being achieved. It is essential to protect the data information before it gets distributed to multi-cloud providers. To protect the privacy, clients’ information must be identi ...
... over the entire data (Aggarwal and Yu 2008). Despite much research a method with satisfactory privacy settings are far from being achieved. It is essential to protect the data information before it gets distributed to multi-cloud providers. To protect the privacy, clients’ information must be identi ...
An adaptive modular approach to the mining of sensor
... Transform the set of n input variables m variables , m
... Transform the set of n input variables m variables , m
Predicting WWW Surfing Using Multiple Evidence Combination
... the LRS model [9]. However, our approach differs from them in the following ways. First, with WhatNext, the ngram based algorithm ignores all the n-grams with length less than four. This eliminates some important data that could well be helpful for predicting user surfing paths. Similarly, the LRS a ...
... the LRS model [9]. However, our approach differs from them in the following ways. First, with WhatNext, the ngram based algorithm ignores all the n-grams with length less than four. This eliminates some important data that could well be helpful for predicting user surfing paths. Similarly, the LRS a ...
THE CONSTRUCTION AND EXPLOITATION OF ATTRIBUTE
... scheme of knowledge representation which is domain shareable and reusable. An algorithm is developed to implement the extraction of taxonomies from an existing ontology. Apart from obtaining the taxonomies from the pre-existing knowledge, we also consider a way of automatic generation. Some typical ...
... scheme of knowledge representation which is domain shareable and reusable. An algorithm is developed to implement the extraction of taxonomies from an existing ontology. Apart from obtaining the taxonomies from the pre-existing knowledge, we also consider a way of automatic generation. Some typical ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.