This paper focuses on the problem of short text summarization on the comment stream of a specific message from social network services (SNS). Due to the high popularity of SNS, the quantity of comments may increase at a high rate right after a social message is published. Motivated by the fact that users may desire to get a brief understanding of a comment stream without reading the whole comment list, we attempt to group comments with similar content together and generate a concise opinion summary for this message. Since distinct users will request the summary at any moment, existing clustering methods cannot be directly applied and cannot meet the real-time need of this application. In this paper, we model a novel incremental clustering problem for comment stream summarization on SNS. Moreover, we propose IncreSTS algorithm that can incrementally update clustering results with latest incoming comments in real time. Furthermore, we design an at-a-glance visualization interface to help users easily and rapidly get an overview summary. From extensive experimental results and a real case demonstration, we verify that IncreSTS possesses the advantages of high efficiency, high scalability, and better handling outliers, which justifies the practicability of IncreSTS on the target problem.

Place query is one of the most fundamental applications, and traditional use cases include finding the exact spatial location of a place and searching for a specific type of places in a given spatial range. On the other hand, there is another possibility that you may want to recommend a visited place to friends but forget the complete name of it. You have vague impressions on it and only remember the information of the place type, the rough range of the place, and some places near it. To enable the capability of query by impression that has not been fully explored in the literature, we define a new place query problem called Place Query with Adjacency Constraints (abbreviated as PQAC). We propose a naive approach and two enhancement algorithms, distance pre-calculating algorithm and grid indexing algorithm, to achieve greater efficiency that can satisfy the real-time need of this place query service. We implement the Query By Impression (abbreviated as QBI) system with a real metropolitan place dataset consisting of more than 40,000 place records from Google Place API. Several experiments are conducted to validate the efficiency and effectiveness of the proposed QBI system.

Online social network services, such as Facebook and Twitter, have become increasingly popular recently. More and more users are accustomed to regularly reading the latest news feeds and interacting with friends on these social websites. However, when the number of friends increases to a large extent, users will receive hundreds of messages in a day and may be overwhelmed by the information overload. To alleviate this problem, we propose a novel visualization technique for social news feeds summarization on social web services. The proposed system SocFeedViewer can produce an egocentric network graph based on the news feeds generated in an arbitrary period of time. This graph provides an overview of those who have generated news feeds during this time period. To enhance the reading experience, we incorporate community detection, connectivity analysis, and importance analysis into our system to make users capable of preferentially surfing news feeds that are more significant and interesting.

Recently, uploading photos and adding identity tags on social network services are prevalent. Although some researchers have considered leveraging context to facilitate the process of tagging, these approaches still rely mainly on face recognition techniques that use visual features of photos. However, since the computational and storage costs of these approaches are generally high, they cannot be directly applicable to large-scale web services. To resolve this problem, we explore using only social network context to generate the top-k list of photo identity tag suggestion. The proposed method is based on various co-occurrence contexts that are related to the question of who may appear in this photo. An efficient ranking algorithm is designed to satisfy the real-time needs of this application. We utilize public album data of 400 volunteers from Facebook to verify that our approach can efficiently provide accurate suggestions with less additional storage requirement.

Many image search engines nowadays still struggle with the semantic gap between low level image features and high level image concepts. Some solutions are proposed to bridge the gap by using surrounding texts of images or by adding tags on images by single user. However, they can only provide obscure or limited information about images. Another problem is that users may not know exactly what they want when they search for images. In this work, we proposed a Progressive Image Search And Recommendation system, named as PISAR, to reduce the semantic gap by incorporating the auto-interpretation and user behavior. PISAR is able to progressively improve the interpretation of images and provide a list of recommendation. The evaluation results show that with the help of auto-interpretation and user behavior, the performance of search results and recommendation results can be progressively improved.

E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database, formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not effective enough in near-duplicate detection. In this paper, we propose a novel e-mail abstraction scheme, which considers e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams. Moreover, we design a complete spam detection system Cosdes (standing for COllaborative Spam DEtection System), which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection. We evaluate Cosdes on a live data set collected from a real e-mail server and show that our system outperforms the prior approaches in detection results and is applicable to the real world.

Prior studies on collaborative spam filtering with near-duplicate similarity matching scheme mainly represent each email by a succinct abstraction derived from email content text. Since these abstractions of emails cannot fully catch the evolving nature of spams, we propose in this paper a novel email abstraction scheme, which considers email layout structure to represent emails. With the proposed abstraction, we design a near-duplicate matching scheme to efficiently match each incoming email with a huge spam database.

Although there have been many recent studies on the mining of sequential patterns in a static database and in a database with increasing data, these works, in general, do not fully explore the effect of deleting old data from the sequences in the database. When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Even worse, the obsolete sequential patterns that are not frequent recently may stay in the reported results. In practice, users are usually more interested in the recent data than the old ones. To capture the dynamic nature of data addition and deletion, we propose a general model of sequential pattern mining with a progressive database while the data in the database may be static, inserted, or deleted. In addition, we present a progressive algorithm Pisa, which stands for progressive mining of sequential patterns, to progressively discover sequential patterns in defined time period of interest (POI). The POI is a sliding window continuously advancing as the time goes by. Pisa utilizes a progressive sequential tree to efficiently maintain the latest data sequences, discover the complete set of up-to-date sequential patterns, and delete obsolete data and patterns accordingly. The height of the sequential pattern tree proposed is bounded by the length of POI, thereby effectively limiting the memory space required by Pisa that is significantly smaller than the memory needed by the alternative method, direct appending (DirApp). Note that the sequential pattern mining with a static database and with an incremental database are special cases of the progressive sequential pattern mining. By changing start time and end time of the POI, Pisa can easily deal with a static database or an incremental database as well. Complexity of algorithms proposed is analyzed. The experimental results show that Pisa not only significantly outperforms the prior methods in execution time by orders of magnitude but also possesses graceful scalability.

Automatic semantic concept detection in video is important for effective content-based video retrieval and mining and has gained great attention recently. In this paper, we propose a general post-filtering framework to enhance robustness and accuracy of semantic concept detection using association and temporal analysis for concept knowledge discovery. Co-occurrence of several semantic concepts could imply the presence of other concepts. We use association mining techniques to discover such inter-concept association relationships from annotations. With discovered concept association rules, we propose a strategy to combine associated concept classifiers to improve detection accuracy. In addition, because video is often visually smooth and semantically coherent, detection results from temporally adjacent shots could be used for the detection of the current shot. We propose temporal filter designs for inter-shot temporal dependency mining to further improve detection accuracy. Experiments on the TRECVID 2005 dataset show our post-filtering framework is both efficient and effective in improving the accuracy of semantic concept detection in video. Furthermore, it is easy to integrate our framework with existing classifiers to boost their performance.

When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. In practice, users are usually more interested in the recent data than the old ones. To capture the dynamic nature of data addition and deletion, we propose a general model of sequential pattern mining with a progressive database. In addition, we present a progressive concept to progressively discover sequential patterns in recent time period of interest.