자연주의적 탐구조사(Naturalistic Inquiry)

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.In this article, we show using two simple attacks that a k-anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called e-diversity that can defend against such attacks. In addition to building a formal foundation for e-diversity, we show in an experimental evaluation that e-diversity is practical and can be implemented efficiently.

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L-diversity: Privacy beyond k-anonymity

We present an algorithm for the hidden-surface elimination problem for rectangles, which is also known as window rendering. The time complexity of our algorithm is dependent on both the number of input rectangles, n, and on the size of the output, κ. Our algorithm obtains a trade-off between these two components, in that its running time is O(r(n1+1/r+κ)), where 1≤r≤log n is a tunable parameter. By using this method while adjusting the parameter r “on the fly” one can achieve a running time that is O(n log n+κ(log n/log(1+κ/n))). Note that when κ is Θ(n), this achieves an O(n log n) running time, and when κ is Θ(n1+e) for any positive constant ɛ, then this achieves an O(κ) running time, both of which are optimal.

An Input-Size/Output-Size Trade-Off in the Time-Complexity of Rectilinear Hidden Surface Removal (Preliminary Version)

In this paper we study the problem of declustering two-dimensional datasets with replication over parallel devices to improve range query performance. The related problem of declustering without replication has been well studied. It has been established that strictly optimal declustering schemes do not exist if data is not replicated. In addition to the usual problem of identifying a good allocation, the replicated version of the problem needs to address the issue of identifying a good retrieval schedule for a given query. We address both problems in this paper. An efficient algorithm for finding a lowest cost retrieval schedule is developed. This algorithm works for any query, not just range queries. Two replicated placement schemes are presented - one that results in a strictly optimal allocation, and another that guarantees a retrieval cost that is either optimal or 1 more than the optimal for any range query.

Optimal parallel I/O for range queries through replication

A mobile portable device will often make queries, to a remote database, that depend on its location: It may ask for the nearest coffee shop, restaurant, pharmacy, etc. For privacy reasons, the mobile unit may not wish to disclose its precise location to the remote database - while it is unavoidable that the cell phone company already knows the rough location of the customer ("somewhere in Lafayette "), it is quite another matter if the customer's precise location can be tracked over tune through his pattern of location-dependent queries to the remote database. This work describes an efficient protocol, between the client and database, through which a client can learn the answer to its location-dependent query without revealing to the remote database anything about his location, other than what the database can infer from the answer it gives to the query (which is unavoidable). We also analyze the performance of some other, simpler solutions, that do not require the database to run a protocol with the client, but that can reveal more information about the private location and also introduce inaccuracies in the answer - we quantify how much error these simpler schemes introduce in the answer.

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Privacy-preserving location-dependent query processing

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1361&context=cstech

Parallel strong orientation of an undirected graph

The SQL group-by operator plays an important role in summarizing and aggregating large datasets in a data analytics stack. While the standard group-by operator, which is based on equality, is useful in several applications, allowing similarity aware grouping provides a more realistic view on real-world data that could lead to better insights. The Similarity SQL-based Group-By operator (SGB, for short) extends the semantics of the standard SQL Group-by by grouping data with similar but not necessarily equal values. While existing similarity-based grouping operators efficiently realize these approximate semantics, they primarily focus on one-dimensional attributes and treat multi-dimensional attributes independently. However, correlated attributes, such as in spatial data, are processed independently, and hence, groups in the multi-dimensional space are not detected properly. To address this problem, we introduce two new SGB operators for multi-dimensional data. The first operator is the clique (or distance-to-all) SGB, where all the tuples in a group are within some distance from each other. The second operator is the distance-to-any SGB, where a tuple belongs to a group if the tuple is within some distance from any other tuple in the group. Since a tuple may satisfy the membership criterion of multiple groups, we introduce three different semantics to deal with such a case: (i) eliminate the tuple, (ii) put the tuple in any one group, and (iii) create a new group for this tuple. We implement and test the new SGB operators and their algorithms inside PostgreSQL. The overhead introduced by these operators proves to be minimal and the execution times are comparable to those of the standard Group-by. The experimental study, based on TPC-H and a social check-in data, demonstrates that the proposed algorithms can achieve up to three orders of magnitude enhancement in performance over baseline methods developed to solve the same problem.

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Mikhail J. Atallah

Papers

An Input-Size/Output-Size Trade-Off in the Time-Complexity of Rectilinear Hidden Surface Removal (Preliminary Version)

Optimal parallel I/O for range queries through replication

Privacy-preserving location-dependent query processing

Parallel strong orientation of an undirected graph

Similarity Group-by Operators for Multi-Dimensional Relational Data