Rectangles are used to approximate objects, or sets of objects, in a plethora of applications, systems and index structures. Many tasks, such as nearest neighbor search and similarity ranking, require to decide if objects in one rectangle A may, must, or must not be closer to objects in a second rectangle B, than objects in a third rectangle R. To decide this relation of "Spatial Domination" it can be shown that using minimum and maximum distances it is often impossible to detect spatial domination. This spatial gem provides a necessary and sufficient decision criterion for spatial domination that can be computed efficiently even in higher dimensional space. In addition, this spatial gem provides an example, pseudocode and an implementation in Python.

Abstract: The increasing tendency across scientific disciplines to write multi authored papers [1,2] makes the issue of the sequence of contributors’ names a major topic both in terms of reflecting actual contributions and in a posteriori assessments by evaluation committees. The reviewers aware that there are different cultures to authorship order. The usual and informal practice of giving the whole credit (impact factor) to each author of a multi authored paper is not adequate and over emphasizes the minor contributions of many authors. Similarly, evaluation of authors according to citation frequencies means often overrating resulting from high-impact but multi authored publications. Teja Tscharntke et al. [72] proposed that four methods. Like as SDC,EC, FLAE, and PCI. Comparison of the credit for contributions to this study under the four different models has been suggested. The proposed systems, such as Individual Frequency (IF) and Weighted Frequency (WF), have no repeated impact for each position.

At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.

Any paper proposing a new algorithm should come with an evaluation of efficiency and scalability (particularly when we are designing methods for “big data”). However, there are several (more or less serious) pitfalls in such evaluations. We would like to point the attention of the community to these pitfalls. We substantiate our points with extensive experiments, using clustering and outlier detection methods with and without index acceleration. We discuss what we can learn from evaluations, whether experiments are properly designed, and what kind of conclusions we should avoid. We close with some general recommendations but maintain that the design of fair and conclusive experiments will always remain a challenge for researchers and an integral part of the scientific endeavor.

Heterogeneous information networks consist of rich information with many typed-links and typed-objects. Nowadays, finding useful knowledge from large information networks has attracted the attention of a large number of researchers. Some famous ranking algorithms like P-PageRank, PathSim and SimRank have been proposed to find the Top-K similar objects. However, SimRank has very high computational complexity while PathSim only does similarity measurement based on a single meta path. In this paper, we develop a novel HighSim algorithm, which integrates the PathSim algorithm and the basic methodology in LINE algorithm, to leverage the similarity ranking by considering both the research topics and the venues of published papers of different authors. In specific, we use PathSim based on the meta path Author-Paper-Venue-Paper-Author (AVPVA) to find the similarity of the venues of published papers. And LINE is used to find the similar research topics of different authors through their cited papers, i.e. references. Then we use the dataset in bibliographic networks extracted from DBLP to evaluate the performance of our new algorithm. The results show the effectiveness and flexibility of our proposed algorithm.

In this paper, the problem of creating an accurate 3D grid map by an autonomous mobile robot is addressed. The proposed algorithm focuses on two aspects of 3D mapping, an incremental and online segmentation process for surface extraction and a surface analysis based on uncertainty. The algorithm is based on an uncertain grid formulation that is used to store 3D point clouds collected by a robot with known poses. Unlike most grid based approaches that consider the distribution of the spatial attributes of the 3D points only for assigning these points into 3D cells, the proposed approach uses the uncertain 3D grid structure and propagates the uncertainty of the raw point clouds to the normal vectors and estimates the uncertainty of the plane parameters. For efficient surface extraction a state of the art stream clustering algorithm is employed. Experiments show that this grid formulation captures the actual distribution of points in the environment and leads to spatial models which contain additional information about their uncertainty.

Social media are a popular source for live textual data. This data poses several challenges due to its size, velocity, and heterogeneity. Existing methods for emerging topic detection often are only able to detect events of a global magnitude such as natural disasters, or they can only monitor user-selected keywords or a curated set of hashtags. Interesting emerging topics may, however, be of much smaller magnitude and may involve the combination of two or more words that are not yet known in beforehand. We present several contributions introduced in previous work [1, 2]: (i) A signi cance measure that can detect emerging topics early, long before they evolve into hot tags , by drawing upon experience from outlier detection. (ii) An e cient online algorithm to track these statistics for all words and wordpairs with only a xed amount of memory, and without prede ned keywords. (iii) The clustering of the detected co-trends into larger topics, because a single event will cause multiple word combinations to trend at the same time. (iv) How to incorporate location information into this process to both allow reporting the locality of events as well as detecting local-only geo-textual patterns. The signi cance score provides an estimated frequency and standard deviation of words, word-pairs, and word-location information on the data stream at minimal cost. It allows for normalization across location, culture, and language and enables the detection of change events both in already frequent and not previously seen combinations. In contrast to earlier work, it can monitor every word at every location with only a xed amount of memory, compare the values to statistics from earlier data, and immediately report signi cant deviations with minimal delay. The algorithm is capable of reporting Breaking News in real-time as they happen in social media around the world. Location is modeled at di erent granularities, such that events can be detected at a city, country, or global level by incorporating OpenStreetMap data, or at particular coordinates.

The analysis of social media data poses several challenges: first of all, the data sets are very large, secondly they change constantly, and third they are heterogeneous, consisting of text, images, geographic locations and social connections. In this article, we focus on detecting events consisting of text and location information, and introduce an analysis method that is scalable both with respect to volume and velocity. We also address the problems arising from differences in adoption of social media across cultures, languages, and countries in our event detection by efficient normalization. We introduce an algorithm capable of processing vast amounts of data using a scalable online approach based on the SigniTrend event detection system, which is able to identify unusual geo-textual patterns in the data stream without requiring the user to specify any constraints in advance, such as hashtags to track: In contrast to earlier work, we are able to monitor every word at every location with just a fixed amount of memory, compare the values to statistics from earlier data and immediately report significant deviations with minimal delay. Thus, this algorithm is capable of reporting "Breaking News" in real-time. Location is modeled using unsupervised geometric discretization and supervised administrative hierarchies, which permits detecting events at city, regional, and global levels at the same time. The usefulness of the approach is demonstrated using several real-world example use cases using Twitter data.

The analysis of social media data poses several challenges: first of all, the data sets are very large, secondly they change constantly, and third they are heterogeneous, consisting of text, images, geographic locations and social connections. In this article, we focus on detecting events consisting of text and location information, and introduce an analysis method that is scalable both with respect to volume and velocity. We also address the problems arising from differences in adoption of social media across cultures, languages, and countries in our event detection by efficient normalization.