Sep 29, 2013 Comments Off on
Jul 17, 2013 Comments Off on Organizers
Organizers
Kunihiko Sadakane (National Institute of Informatics), Japan
Wing-Kin Sung (National University of Singapore), Singapore
Jul 17, 2013 Comments Off on Participants
Participants
Rajeev Raman | University of Leicester |
Venkatesh Raman | The Institute of Mathematical Sciences |
Srinivasa Rao Satti | Seoul National University |
Ian Munro | University of Waterloo |
Moshe Lewenstein | Bar-Ilan University |
Gonzalo Navarro | University of Chile |
Francisco Claude | Universidad Diego Portales / Akori |
Travis Gagie | University of Helsinki |
Simon Gog | The University of Melbourne |
Jesper Larsson | IT University of Copenhagen |
Sebastiano Vigna | Università degli Studi di Milano |
Giuseppe Ottaviano | University of Pisa |
Ankur Gupta | Butler University |
Hiroshi Sakamoto | Kyutech |
Tetsuo Shibuya | University of Tokyo |
Taku Onodera | University of Tokyo |
Takuya Akiba | University of Tokyo |
Hiroki Arimura | Hokkaido University |
Shin-ichi Minato | Hokkaido University |
Takuya Kida | Hokkaido University |
Shuhei Denzumi | Hokkaido University |
Yasuo Tabei | Japan Science and Technology Agency |
Koji Tsuda | AIST |
Anish Shrestha | Department of Computational Biology, University of Tokyo |
Martin Frith | CBRC, AIST |
Takeaki Uno | NII |
Alexander Bowe | NII |
Atsushi Koike | NII |
Wing-Kin Sung | National University of Singapore |
Kunihiko Sadakane | NII |
Jul 17, 2013 Comments Off on Schedule
Schedule
Arrival Day ? Sep 26
15:00 – 19:00 Check-in
19:00 ‐21:30??Welcome Reception
Day 1 ? Sep 27
7:30 ‐9:00??Breakfast
9:00 ‐10:30??Session 1
Ian Munro | Succinct data structures for representing equivalence classes |
Rajeev Raman | Encodings for top-k and range selection |
Moshe Lewenstein | Two Dimensional Range Minimum Queries and Fibonacci Lattices |
11:00 ‐12:00??Session 2
Sebastiano Vigna | Quasi-Succinct Indices |
Simon Gog | Integer Alphabet-based Self-Indexes at Terabyte Scale |
12:00 – 14:00 Lunch
14:00 – 15:40 Session 3
Travis Gagie | An Alignment-Based Index for Genomic Datasets |
Martin Frith | Bio-sequence similarity search with spaced suffix arrays and subset suffix arrays |
Alexander Bowe | Succinct de Bruijn Graphs |
Taku Onodera | Detecting Superbubbles in Assembly Graphs |
16:00 – 18:00 Session 4 ? ? Discussions
18:00 – 19:30 Dinner
Day 2 ? Sep 28
7:30 ‐9:00??Breakfast
9:00 ‐11:00??Session 5
Srinivasa Rao Satti | Selection from Read-Only Memory with Limited Workspace |
Venkatesh Raman | Improved Selection Algorithms for Integers in Read-only Memory and Restore Models |
Ankur Gupta | Online Multiselection |
Gonzalo Navarro | Document Retrieval on General Sequences |
11:30 ‐12:30??Session 6
Francisco Claude | Adaptive Data Structures for Permutations and Binary Relations |
Giuseppe Ottaviano | Compressed tries and top-k string completion |
12:30 – 14:00 Lunch
14:00 -?16:20 Session 7
Anish Shrestha | New Challenges to Processing DNA Data from Modern-day Sequencers |
Yasuo Tabei | Succinct data structures for scalable similarity search in ChemBioinformatics |
Tetsuo Shibuya | Fast Indexing Method for Protein 3-D Structure Searching |
Jesper Larsson | Encoding and modeling for set compression |
Takuya Akiba | Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned?Landmark Labeling |
16:20 – 18:00 Session 8 ? ? ? Discussions
18:00 – 19:30 Dinner
Day 3 ? Sep 29
7:30 ‐9:00??Breakfast
9:00 ‐11:00??Session 9
Hiroshi Sakamoto | An application of stream compression |
Shirou Maruyama | Fully-Online Grammar Compression |
Hiroki Arimura | Faster Broad-Word Pattern Matching Algorithms for?Regular Expressions and Trees |
Takuya Kida | Data Compression using Variable-to-Fixed Length Codes |
11:00 – 11:30 Group Photo Shooting
11:30 ‐12:30 ?Session 10
Shin-ichi Minato | ZDD-Based Representation for Large-Scale Sparse Datasets?and Z-Skip-Links for Fast Traversal |
12:30 – 13:30 Lunch
13:30 -?19:00 Excursion to Kamakura
19:00 – 21:30 Banquet
Day 4 ? Sep 30
7:30 ‐9:00??Breakfast
( ? ? ? ? ?- 10:00 ?Check-out)
9:00 ‐10:30??Session 11
Shuhei Denzumi | DenseZDD: A Fast and Compact Data Structure for Family of Sets &PathSeqBDD:?A DAG Index based on Sequence BDD |
Koji Tsuda | Enumeration Algorithms and Statistical Significance |
Takeaki Uno | Similarity based Approach for Compression of Noisy Data |
10:30 ‐12:00??Session 12 ? ? Disucssions
12:00 – 13:30 Lunch
13:30 ? ? ? ? ? ? ? Dismiss
Jul 17, 2013 Comments Off on Overview
Overview
Big Data are structured and unstructured datasets whose size is in the order of billions?or trillions. Because of their diversity and size, it is difficult to store, search and?analyze them. This meeting therefore focuses on algorithms and data structures for?efficient manipulation of Big Data. Especially, the meeting is devoted to compact data?structures for managing Big Data.
Typical examples of big data are genomic sequences and gene expression data, Web and?SNS data, sensor data in intelligent transport systems, etc. Traditional data structures?do not scale to handle such data, and therefore we should design new data structures to?handle them.
Although the amount of data explodes, the amount of the underlying information inside?the data may not be exploded. It is observed that many big datasets are redundant. In?the Web, many webpages were copies of others. In global positioning system (GPS),?GPS position data change continuously, which can be compressed using differential?encoding. In genomics, although different individuals have different genomes, the?individual genomes have highly similarity. Therefore we can compress such data by?identifying the similar parts. After the data is compressed, other issues are how to?access and search them efficiently. Traditional data structures are not designed to?handle compressed data and they may not manipulate Big Data well because the size of?the data structures exceeds the limit of memory usage, or searching time increases due?to their size. To handle these problems, researchers have worked on developing?compact data structures. Such data structures are also called compressed or succinct?data structures. They are much smaller than standard data structures, while keeping?the same access time to data in theory. However actual performance of such compact?data structures for storing Big Data is unknown or unsatisfactory.
The aim of this workshop is to bring together researchers active in the areas of compact?data structures to exchange ideas for handling Big Data. We will discuss methods for?compressing and storing Big Data. We will also discuss how to design time- and?space-efficient data structures for them Through discussion and sharing knowledge, we?hope to promote collaborations and further improve data structures for Big Data.
Jul 6, 2013 Comments Off on Railway Time Table
Railway Time Table
From Tokyo Narita airport (NRT) to the Shonan Village, you can take JR trains. ?Some local trains (Soubu and Yokosuka lines) directly go to Zushi station, where you can take a bus to the village. ?It takes two hours and fifteen minutes from the airport to Zushi station. ?The trains have toilets and first-class cars (GREEN CAR).
Narita Express (NEX) is faster than local trains, but you have to change the train to local (Yokosuka) line. ?You can change trains at Tokyo or Musashi-Kosugi station. ?Trains will depart from the same platform as NEX at the transfer stations. ?Please pay attention to the destination of the trains; you can take trains bound for only Zushi, Yokosuka, or Kurihama?station.
The following table shows connections of trains and buses. ?Each row represents a journey. ?All trains leave Narita airport station (Terminal 1), then stop at Airport Terminal 2 station. ?They also stop at Tokyo and Musashi-Kosugi. ?However their arrival and departure times are not shown in the table if train change is not necessary. ?Prices are for trains and they do not include the bus fare (340 yen). ?Note that you should buy a train ticket from the airport to Zushi station (2,210 yen) even if you change trains on the way. ?In the table, “rush at Zushi” means that you may not have enough time to catch the bus, and “bad connection” means that you have to wait for more than 30 minutes for the next bus.
You can also find other routes using airport limousine buses etc. using Google maps.
NRT T1 | NRT T2 | Tokyo | Musashi-Kosugi | Zushi | Shonan | |||||||
dep. | dep. | arr. | dep. | arr. | dep. | arr. | dep. | arr. | Time | Price | Remarks | |
9:02 | 9:04 | 11:48 | 12:02 | 12:32 | 3:30 | 2,210 | ||||||
NEX 10 | 9:15 | 9:19 | 10:38 | 10:44 | 11:30 | 12:02 | 12:32 | 3:17 | 3,670 | ? bad connection | ||
NEX 12 | 9:45 | 9:48 | 11:02 | 11:07 | 11:48 | 12:02 | 12:32 | 2:47 | 3,670 | |||
9:59 | 10:02 | 12:48 | 13:01 | 13:31 | 3:32 | 2,210 | ||||||
NEX 14 | 10:15 | 10:18 | 11:32 | 11:37 | 12:17 | 13:01 | 13:31 | 3:16 | 3,670 | ? bad connection | ||
NEX 16 | 10:45 | 10:48 | 12:02 | 12:06 | 12:48 | 13:01 | 13:31 | 2:46 | 3,670 | |||
10:59 | 11:01 | 13:48 | 14:04 | 14:34 | 3:35 | 2,210 | ||||||
NEX 18 | 11:14 | 11:18 | 12:32 | 12:37 | 13:19 | 14:04 | 14:34 | 3:20 | 3,670 | ? bad connection | ||
NEX 20 | 11:45 | 11:48 | 12:44 | 12:53 | 13:58 | 14:04 | 14:34 | 2:49 | 3,670 | ? rush at Zushi | ||
11:57 | 12:00 | 14:48 | 14:52 | 15:22 | 3:25 | 2,210 | ? rush at Zushi | |||||
NEX 22 | 12:19 | 12:22 | 13:32 | 13:37 | 14:19 | 14:52 | 15:22 | 3:03 | 3,670 | |||
12:27 | 12:29 | 15:08 | 15:38 | 16:08 | 3:41 | 2,210 | ||||||
12:59 | 13:01 | 15:42 | 16:37 | 17:07 | 4:08 | 2,210 | ? bad connection | |||||
NEX 24 | 13:14 | 13:17 | 14:32 | 14:37 | 15:19 | 15:38 | 16:08 | 2:54 | 3,670 | |||
NEX 26 | 13:45 | 13:48 | 15:02 | 15:06 | 15:48 | 16:37 | 17:07 | 3:22 | 3,670 | ? bad connection | ||
NEX 28 | 14:18 | 14:20 | 15:14 | 15:24 | 16:29 | 16:37 | 17:07 | 2:49 | 3,670 | |||
14:31 | 14:33 | 17:09 | 17:49 | 18:19 | 3:48 | 2,210 | ? bad connection | |||||
NEX 30 | 14:44 | 14:48 | 16:02 | 16:11 | 17:00 | 17:05 | 17:35 | 2:51 | 3,670 | ? rush at Zushi | ||
14:58 | 15:00 | 17:42 | 17:49 | 18:19 | 3:21 | 2,210 | ||||||
NEX 32 | 15:14 | 15:18 | 16:32 | 16:37 | 17:22 | 17:49 | 18:19 | 3:05 | 3,670 | |||
NEX 34 | 15:44 | 15:47 | 17:02 | 17:06 | 17:48 | 18:34 | 19:04 | 3:20 | 3,670 | ? bad connection | ||
15:57 | 16:00 | 18:42 | 19:02 | 19:32 | 3:35 | 2,210 | ||||||
NEX 36 | 16:18 | 16:21 | 17:38 | 17:45 | 18:30 | 18:34 | 19:04 | 2:46 | 3,670 | ? rush at Zushi | ||
16:32 | 16:35 | 19:20 | 19:31 | 20:01 | 3:29 | 2,210 | ||||||
NEX 38 | 16:44 | 16:48 | 18:02 | 18:06 | 18:49 | 19:02 | 19:32 | 2:48 | 3,670 | |||
16:57 | 17:00 | 19:47 | 20:20 | 20:50 | 3:53 | 2,210 | ||||||
NEX 40 | 17:16 | 17:19 | 18:37 | 18:42 | 19:25 | 19:31 | 20:01 | 2:45 | 3,670 | |||
NEX 42 | 17:44 | 17:47 | 19:06 | 19:13 | 19:55 | 20:20 | 20:50 | 3:06 | 3,670 | |||
17:59 | 18:02 | 20:48 | 21:10 | 21:40 | 3:41 | 2,210 | ||||||
NEX 44 | 18:15 | 18:19 | 19:36 | 19:43 | 20:25 | 20:45 | 21:15 | 3:00 | 3,670 | |||
18:30 | 18:33 | 21:18 | 21:41 | 22:11 | 3:41 | 2,210 | ||||||
NEX 46 | 18:48 | 18:52 | 20:09 | 20:16 | 20:58 | 21:10 | 21:40 | 2:52 | 3,670 | |||
19:02 | 19:04 | 21:51 | 22:17 | 22:47 | 3:45 | 2,210 | ||||||
NEX 48 | 19:12 | 19:15 | 20:32 | 20:36 | 21:18 | 21:41 | 22:11 | 2:59 | 3,670 | |||
19:35 | 19:37 | 22:13 | 22:17 | 22:47 | 3:12 | 2,210 | ||||||
NEX 50 | 19:46 | 19:49 | 20:51 | 21:00 | 22:00 | 22:17 | 22:47 | 3:01 | 3,670 |