In this paper, we propose a method for reconstructing 3D sequential patterns from multiple images without knowing exact image correspondences and without calibrating linear camera sensitivity parameters on intensity. The sequential pattern is defined as a series of colored 3D points. We assume that the series of the points are obtained in multiple images, but the correspondence of individual points is not known among multiple images. For reconstructing sequential patterns, we consider a camera projection model which combines geometric and photometric information of objects. Furthermore, we consider camera projections in the frequency space. By considering the multi-view relationship on the new projection model, we show that the 3D sequential patterns can be reconstructed without knowing exact correspondence of individual image points in the sequential patterns; moreover, the recovered 3D patterns do not suffer from changes in linear camera sensitivity parameters. The efficiency of the proposed method is tested using real images.

Developers often face difficulties while using APIs. API usage patterns can aid them in using APIs efficiently, which are extracted from source code stored in software repositories. Previous approaches have mined repositories to extract API usage patterns by simply applying data mining techniques to the collection of method invocations of API objects. In these approaches, respective functional roles of invoked methods within API objects are ignored. The functional role represents what type of purpose each method actually achieves, and a method has a specific predefined order of invocation in accordance with its role. Therefore, the simple application of conventional mining techniques fails to produce API usage patterns that are helpful for code completion. This paper proposes an improved approach that extracts API usage patterns at a higher abstraction level rather than directly mining the actual method invocations. It embraces a multilevel sequential mining technique and uses categorization of method invocations based on their functional roles. We have implemented a mining tool and an extended Eclipse's code completion facility with extracted API usage patterns. Evaluation results of this tool show that our approach improves existing code completion.

The mining problem over data streams has recently been attracting considerable attention thanks to the usefulness of data mining in various application fields of information science, and sequence data streams are so common in daily life. Therefore, a study on mining sequential patterns over sequence data streams can give valuable results for wide use in various application fields. This paper proposes a new framework for mining novel interesting sequential patterns over a sequence data stream and a mining method based on the framework. Assuming that a sequence with small time-intervals between its data elements is more valuable than others with large time-intervals, the novel interesting sequential pattern is defined and found by analyzing the time-intervals of data elements in a sequence as well as their orders. The proposed framework is capable of obtaining more interesting sequential patterns over sequence data streams whose data elements are highly correlated in terms of generation time.

Overcoming the highly organized and coordinated malware threats by botnets on the Internet is becoming increasingly difficult. A honeypot is a powerful tool for observing and catching malware and virulent activity in Internet traffic. Because botnets use systematic attack methods, the sequences of malware downloaded by honeypots have particular forms of coordinated pattern. This paper aims to discover new frequent sequential attack patterns in malware automatically. One problem is the difficulty in identifying particular patterns from full yearlong logs because the dataset is too large for individual investigations. This paper proposes the use of a data-mining algorithm to overcome this problem. We implement the PrefixSpan algorithm to analyze malware-attack logs and then show some experimental results. Analysis of these results indicates that botnet attacks can be characterized either by the download times or by the source addresses of the bots. Finally, we use entropy analysis to reveal how frequent sequential patterns are involved in coordinated attacks.

In this paper, we introduce a new sequential pattern, the Interactive User Sequence Pattern (IUSP). This pattern is useful for grouping highly interrelated users in one-way communications such as e-mail, SMS, etc., especially when the communications include many spam users. Also, we propose an efficient algorithm for discovering IUSPs from massive one-way communication logs containing only the following information: senders, receivers, and dates and times. Even though there is a difficulty in that our new sequential pattern violates the Apriori property, the proposed algorithm shows excellent processing performance and low storage cost in experiments on a real dataset.