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The smartphone applications have taken place of the web browser and became the user's primary internet entrance. One application's popularity can be measured by its downloading times, and it is valuable for commercial advertising. Identifying app installation packages from network traffic is one of the most feasible approaches to collect these data. But asymmetric routing, incomplete capture and so...
We present a novel approach for detecting malicious user activity in databases. Specifically, we propose a new machine learning algorithm for detecting attacks such as a stolen user account or illegal use by a user. Our algorithm relies on two main components that examine the consistency of a user's activity and compare it with activity patterns learned from past access. The first component tests...
Top-k join is an essential tool for data analysis, since it enables selective retrieval of the k best combined results that come from multiple different input datasets. In the context of Big Data, processing top-k joins over huge datasets requires a scalable platform, such as the widely popular MapReduce framework. However, such a solution does not necessarily imply efficient processing, due to inherent...
Privacy and security of big data is emerging as one among the most relevant research challenges of recent years, also stirred-up by a wide family of critical applications ranging from scientific computing to social network analysis and mining, from data stream management to smart cities, and so forth. Traditionally, the issue of making (even very-large) databases private and secure has a long history...
DBpia is the largest digital-bibliography service provider in Korea. It provides several convenience functions for researchers. DBpia users (i.e., researchers) can search for papers via several search routes such as publications, publishers, authors, and keywords. Although the researchers can exploit the search functions, they may still have a number of search results as candidate papers to read....
This research aims to examine the effectiveness and efficiency of fuzzing hashing algorithm in the identification of similarities in Malware Analysis. More precisely, it will present the benefit of using fuzzy hashing algorithms, such as ssdeep, sdhash, mvHash and mrsh – v2, in identifying similarities in Malware domain. The obtained results will be compared with the traditional and most common Cryptographic...
Knowledge discovery from data is a challenging problem that has significant importance in many different fields such as biology, economics and social sciences. Real-world data is incomplete and ambiguous; moreover, its rapid increase in size complicates the analysis process. Therefore, data reduction techniques that consider data uncertainty are highly required. In this paper, our objective is to...
Meetings are an important communication and coordination activity of teams: status is discussed, new decisions are made, alternatives are considered, details are explained, information is presented, and new ideas are generated. As such, meetings contain a large amount of rich project information that is often not formally documented. Capturing all of this informal meeting information has been a topic...
As software consumption is shifting to mobile platforms, enterprises are looking for efficient ways to reuse their existing legacy systems by exposing their functionalities as services. Mining services from legacy code is therefore an important problem for the enterprises. In this paper we present a technique for mining service candidates from the database applications. Central to our mining technique...
This paper addresses the problem of refining large skylines by introducing a new dominance relationship. Skyline queries are powerful tool to capture user preferences. However in some contexts, the skyline is too large to give any useful insight to the user. In order to solve this problem, we introduce a strong dominance relationship that relies on the relation called “much preferred”. This leads...
Enterprise applications are difficult to test because their intended functionality is either not described precisely enough or described in cumbersome business rules. It takes a lot of effort on the part of a test architect to understand all the business rules and design tests that "cover" them, i.e., exercise all their constituent scenarios. Part of the problem is that it takes a complicated...
IoT/Bigdata is a hot research topic all over the world in recent years and is expecting to change the world greatly in the near future. Comparing with the data in traditional websites, Bigdata from IoT devices have 4 big V-features, i.e., volume, velocity, variety, and veracity. Due to the above four features, it is hard to provide timely services to users by data analysis, especially with the great...
In today's fast-growing information age we have an abundance of text, especially on the web. New information is constantly being generated. Often due to time constraints we are not able to consume all the data available. It is therefore essential to be able to summarize the text so that it becomes easier to ingest, while maintaining the essence and understandability of the information. We aim to design...
We study the novel problem of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy—e.g., an athlete's outstanding performance in a game, or a viral video's impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational...
The growing complexity of embedded systems makes their behavior analysis a challenging task. In this context, tracing appears to be a promising solution as it provides relevant information about the system execution. However, trace management and analysis are hindered by the diversity of trace formats, the incompatibility of trace analysis methods, the problem of trace size and its storage as well...
Association rule discovery, as the kernel task of data mining, has been studied widely. However, most algorithms based on frequent item sets have to scan databases many times. This reduces the algorithms' efficiency. Formal concept analysis is a useful tool in many fields. In this paper, an association rule mining algorithm is proposed based on the formal concept analysis. Through analysis the relationship...
Cloud computing provides the possibility of a solution to the problems caused by the massive amounts of data. As an open source cloud computing platform, Hadoop has been widely used in the commercial. MapReduce model is one of the important parts of Hadoop, and it can support parallel computing and schedule tasks automatically. Because of these, it can improve the efficiency of the configuration while...
We solve the problem of localizing and tracking household objects using a depth-camera sensor network. We design and implement Kin sight that tracks household objects indirectly -- by tracking human figures, and detecting and recognizing objects from human-object interactions. We devise two novel algorithms: (1) Depth Sweep -- that uses depth information to efficiently extract objects from an image,...
Context based Meaning Extraction is a process of finding the correct sense of a word from the sentence. Word Sense Disambiguation (WSD) algorithm is used to remove ambiguity of words and correct domain of a word to be displayed using Word Net Domain. In this paper, conventional methods of WSD such as dictionary and knowledge base, supervised, semi-supervised and unsupervised methods are compared....
In this paper we describe incremental algorithm for generalized one-sided concept lattices based on the Galois connections within Formal Concept Analysis (FCA) framework, which allows to analyse object-attribute models with different structures for truth values of attributes. Therefore, this method provide interesting opportunity for researcher or data analyzer to work with any type of attributes...
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