Takeshi Mishima

Efficient ad-hoc search for personalized PageRank

Personalized PageRank (PPR) has been successfully applied to various applications. In real applications, it is important to set PPR parameters in an ad-hoc manner when finding similar nodes because of dynamically changing nature of graphs. Through interactive actions, interactive similarity search supports users to enhance the efficacy of applications. Unfortunately, if the graph is large, interactive similarity search is infeasible due to its high computation cost. Previous PPR approaches cannot effectively handle interactive similarity search since they need precomputation or approximate computation of similarities. The goal of this paper is to efficiently find the top-k nodes with exact node ranking so as to effectively support interactive similarity search based on PPR. Our solution is Castanet. The key Castanet operations are (1) estimate upper/lower bounding similarities iteratively, and (2) prune unnecessary nodes dynamically to obtain top-k nodes in each iteration. Experiments show that our approach is much faster than existing approaches.

Madeus: Database Live Migration Middleware under Heavy Workloads for Cloud Environment

Database-as-a-service has been gaining popularity in cloud computing because multitenant databases can reduce costs by sharing off-the-shelf resources. However, due to heavy workloads, resource sharing often causes a hot spot; one node is overloaded even while others are not. Unfortunately, a hot spot can lead to violation of service level agreements and destroy customer satisfaction. To efficiently address the hot spot problem, we propose a middleware approach called Madeus that conducts database live migration. To make efficient database live migration possible, we also introduce the lazy snapshot isolation rule (LSIR) that enables concurrently propagating syncsets, which are the datasets needed to synchronize slave with master databases. Madeus provides efficient database live migration by implementing the LSIR under snapshot isolation. Unlike current approaches, Madeus is pure middleware that is transparent to the database management system and based on commodity hardware and software. To demonstrate the superiority of our approach over current approaches, we experimentally evaluated Madeus by using PostgreSQL with the TPC-W benchmark. The results indicate that Madeus achieves more efficient live migration than three other types of middleware approaches, especially under heavy workloads; therefore, it can effectively resolve hot spots.

Optimization of topic estimation for the domain adapted neural network language model

We present a neural network language model adapted for topics fluctuating in broadcast programs. Topic adapted n-gram language models constructed by using latent Dirichlet allocation for topic estimation are widely used. The conventional method estimates topics by separating the corpora into chunks that have few sentences. While the n-gram model uses several preceding words, the recurrent neural network and long short-term memory can learn to store huge amounts of past information in the hidden layers. Consequently, chunks for language models trained by using neural networks may have a longer optimal length than the chunks for language models trained by using the conventional methods. In this paper, the length of chunks and topic estimation process are optimized for the neural network language models. For the topic estimation, k-mean clustering, latent Dirichlet allocation, and word2vec were compared. On the basis of the results of comparison, we designed a neural network language model.

End-to-end neural network modeling for Japanese speech recognition

This study proposes end-to-end neural network modeling to adapt direct speech-to-text decoding to Japanese. End-to-end speech recognition systems using deep neural networks (DNNs) are currently being investigated. These systems do not need intermediate phonetic representation. Instead, many of them utilize Recurrent Neural Networks (RNNs) trained by using much more data than ever before. The end-to-end approach makes acoustic models simpler to train. Typically, previous works have dealt with phonogram labels such alphabetic characters. Ideograms such as Kanji, however, make end-to-end speech recognition more complex. A single Kanji can have multiple readings, such as On-yomi (Chinese reading) and Kun-yomi (Japanese reading). In addition, whereas alphabets have at most 100 labels, Japanese has over 2000 labels to predict, such as Kanji, Hiragana, Katakana, the Roman alphabet, digits, and punctuation marks. To resolve this problem, we attempt to make end-to-end neural network modeling allows speech recognit...