In the United States, Obama's presidency administration has moved forward with Open Government Initiative to open public data. Similarly, Tim Berners-Lee, the inventor of the World Wide Web, has led data.gov.uk in England. With the active movement of these global governments, open public data have let citizens know government policies clearly. However, since it is not trivial for people to clearly understand raw public data, active studies have carried out for good visualization. As an example, in 2009, Andrew Odewahn showed how to visualize U.S. Senate social graphs from 1991 until 2009. Given a polling data set in public, a graph consisting of a set of nodes with edges could be created, where each node is a senator and the edge between two nodes represents the similar voting behavior of the senators. However, since such a graph is considerably dense and complex, it is hard to visualize and also figure out the meaning of the graph. Toward this problem, in this paper, we propose novel graph visualization techniques based on friend matching and hierarchical matching. In our empirical study, we applied our visualization algorithms to the voting data of the 18th National Assembly in Korea, Senate bills in U.S., and U.N. voting data. Then, we showed the effectiveness of our proposed methods through user test.

Phase change memory (PCM) is non-volatile memory that is byte-addressable. It is two to four times denser than DRAM, orders of magnitude better than NAND Flash memory in read latency, and 10 times better than NAND Flash memory in write endurance. However, it still limits the number of write operations to at most 106 times per PCM cell. To extend its lifetime, it is necessary to evenly distribute write operations over all the memory cells. Up to now, the B+-Tree index structure has been used to quickly locate a search key in a relational database management system (RDBMS). All the record keys in each node are sorted and packed upon insertion in, and deletion from, the B+-Tree. In addition, a counter keeps track of the number of valid keys in the B+-Tree. Consequently, a B+-Tree algorithm results in a large number of write operations, which deteriorates the endurance of PCM. This restricts the usage of PCM on a database server and deteriorates performance of database servers. In this paper, we propose a novel PCM-aware B+-Tree index structure, called PB+-Tree, to provide wear-leveling in PCM. According to our experiment results, PB+-Tree is much faster than the existing B+-Tree algorithms for PCM and NAND Flash memory with versatile workloads. More importantly, our scheme also greatly reduces the number of write operations compared to other B+-Tree algorithms. All of these results suggest that PB+-Tree is the B+-Tree algorithm best fitted to PCM.

In email or twitter networks, mining interaction behaviors of actors is a useful task in viral marketing or targeted advertisement. In this work, we define two interaction behaviors. One is engagingness representing the ability of soliciting active r

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive users. This paper proposes four types of models to quantify engagingness and responsiveness of users. These behaviors can be used as features in the email reply order prediction task which predicts the email reply order given an email pair. Our experiments show that engagingness and responsiveness behavior features are more useful than other non-behavior features in building a classifier for the email reply order prediction task. When combining behavior and non-behavior features, our classifier is also shown to predict the email reply order with good accuracy.

In this demo we introduce GuruMine, a pattern mining system for the discovery of leaders, i.e., influential users in social networks, and their tribes, i.e., a set of users usually influenced by the same leader over several actions. GuruMine is built upon a novel pattern mining framework for leaders discovery, that we introduced. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (URLS) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handholds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends actions are sometimes tempted to perform those actions. On the basis of the propagation of such influence, we provided various notion of leaders and developed algorithms for their efficient discovery. GuruMine provides users with a friendly graphical interface for selecting the actions of interest, and the kind of leaders to mine. The set of parameters driving the pattern discovery process can be iteratively refined, and the result is updated, if possible without incurring a completely new computation. Once a set of leaders has been extracted, GuruMine can easily validate them on a set of actions unseen during the pattern mining, by analyzing the portion of network reached by the influence of the selected leaders on the unseen actions. GuruMine also offers various visualizations over the social networks: the propagation of an action, the leaders, their tribes, and the interactions between different leaders and tribes. In this demo we will show: (i) how the pattern mining process can be driven towards the discovery of a good set of leaders, (ii) the ease of use of GuruMine system, and (iii) its outstanding performances on large real-world social networks and actions databases.

Poor quality data is prevalent in databases due to a variety of reasons, including transcription errors, lack of standards for recording database fields, etc. To be able to query and integrate such data, considerable recent work has focused on the record linkage problem, i.e., determine if two entities represented as relational records are approximately the same. Often entities are represented as groups of relational records, rather than individual relational records, e.g., households in a census survey consist of a group of persons. We refer to the problem of determining if two entities represented as groups are approximately the same as group linkage. Intuitively, two groups can be linked to each other if (i) there is high enough similarity between "matching" pairs of individual records that constitute the two groups, and (ii) there is a large fraction of such matching record pairs. In this paper, we formalize this intuition and propose a group linkage measure based on bipartite graph matching. Given a data set consisting of a large number of groups, efficiently finding groups with a high group linkage similarity to an input query group requires quickly eliminating the many groups that are unlikely to be desired matches. To enable this task, we present simpler group similarity measures that can be used either during fast pre-processing steps or as approximations to our proposed group linkage measure. These measures can be easily instantiated using SQL, permitting our techniques to be implemented inside the database system itself. We experimentally validate the utility of our measures and techniques using a variety of real and synthetic data sets.

When non-unique values are used as the identifier of entities, due to their homonym, confusion can occur.In particular, when (part of) "names" of entities are used as their identifier, the problem is often referred to as the name disambiguation problem, where goal is to sort out the erroneous entities due to name homonyms (e.g., if only last name is used as the identifier, one cannot distinguish "Vannevar Bush" from "George Bush").In this paper, in particular, we study the scalability issue of the name disambiguation problem -when (1) a small number of entities with large contents or (2) a large number of entities get un-distinguishable due to homonyms, how to resolve it?We first carefully examine two of the state-of-the-art solutions to the name disambiguation problem, and point out their limitations with respect to scalability.Then, we adapt the multi-level graph partition technique to solve the large-scale name disambiguation problem.Our claim is empirically validated via experimentation -our proposal shows orders of magnitude improvement in terms of performance while maintaining equivalent or reasonable accuracy compared to competing solutions.Formally, using the terms of Table 1, the name disambiguation problem in our setting is defined as follows: Downloaded 08/15/23 to 181.

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.

We present an alternative design, Eff-MM (efficient mobile multicast). For efficiently supporting multicast for mobile hosts on the Internet. Providing multicast services to hosts has become popular and many multicast protocols have been proposed for mobile hosts. In the delivery tree problems occur whenever a member moves. Such problems are non-optimal delivery route and overheads caused by the frequent reconstruction of a multicast routing tree. To solve problems like this, the paper used the tunneling range and search method to routing paths around migration places with a simple message. The result provides the shortest routes for delivery of multicast datagrams to mobile hosts and reduces frequent reconstruction of the multicast tree reconfiguration.