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From "Kevin A. McGrail" <kmcgr...@apache.org>
Subject Re: [Result][Vote] vote for IoTDB incubation proposal
Date Wed, 14 Nov 2018 16:51:20 GMT
Congratulations!  As champion, I think the next steps are:

1 - Xiangdong, Can you confirm the list of mentors on the proposal is
accurate?

2 - Also Xiangdong, Is there anyone else that stepped forward as a mentor
during the voting process that the project wants the IPMC to approve?

3 - Justin, I think you have to request the creation of the podling and
then I as champion work on things like the meta data file from this page,
https://incubator.apache.org/policy/incubation.html, correct?

Regards,
KAM




--
Kevin A. McGrail
VP Fundraising, Apache Software Foundation
Chair Emeritus Apache SpamAssassin Project
https://www.linkedin.com/in/kmcgrail - 703.798.0171


On Wed, Nov 14, 2018 at 6:29 AM hxd <hxdreg@qq.com> wrote:

> Hi,
>
> With 8 +1 binding votes,  2 +1 non-binding votes and No +/-0 or -1 votes,
> this VOTE passes.
>
> Thanks to everyone who voted!
>
> Bellow is a voting tally:
>
> Binding
> Von Gosling
>  Christofer Dutz
>  Kevin A. McGrail
>  Felix Cheung
>  Matt Sticker
>  Joe Witt
>  Justin Mclean
>  Willem Jiang
>
>
> Non-binding
>  Sheng Wu
>  Yang Bo
>
> The vote thread:
> https://lists.apache.org/thread.html/077f029ab2b52a2b19fc8d41c07438f660a8e93dd87b3895d262263c@%3Cgeneral.incubator.apache.org%3E
> <
> https://lists.apache.org/thread.html/077f029ab2b52a2b19fc8d41c07438f660a8e93dd87b3895d262263c@%3Cgeneral.incubator.apache.org%3E>
>
> The proposal: https://wiki.apache.org/incubator/IoTDBProposal <
> https://wiki.apache.org/incubator/IoTDBProposal>
>
> Thanks,
>
> Xiangdong Huang
>
>
> > 在 2018年11月7日,下午3:46,hxd <hxdreg@qq.com> 写道:
> >
> > Hi,
> >
> > Sorry for the previous mail with bad format.
> > I'd like to call a VOTE to accept IoTDB project, a database for managing
> large amounts of time series data  from IoT sensors in industrial
> applications, into the Apache Incubator.
> > The full proposal is available on the wiki:
> https://wiki.apache.org/incubator/IoTDBProposal
> > and it is also attached below for your convenience.
> >
> > Please cast your vote:
> >
> >   [ ] +1, bring IoTDB into Incubator
> >   [ ] +0, I don't care either way,
> >   [ ] -1, do not bring IoTDB into Incubator, because...
> >
> > The vote will open at least for 72 hours.
> >
> > Thanks,
> > Xiangdong Huang.
> >
> >
> > = IoTDB Proposal  =
> > v0.1.1
> >
> >
> > == Abstract ==
> > IoTDB is a data store for managing large amounts of time series data
> such as timestamped data from IoT sensors in industrial applications.
> >
> > == Proposal ==
> > IoTDB is a database for managing large amount of time series data with
> columnar storage, data encoding, pre-computation, and index techniques. It
> has SQL-like interface to write millions of data points per second per node
> and is optimized to get query results in few seconds over trillions of data
> points. It can also be easily integrated with Apache Hadoop MapReduce and
> Apache Spark for analytics.
> >
> > == Background ==
> >
> > A new class of data management system requirements is becoming
> increasingly important with the rise of the Internet of Things. There are
> some database systems and technologies aimed at time series data
> management.  For example, Gorilla and InfluxDB which are mainly built for
> data centers and monitoring application metrics. Other systems, for
> example, OpenTSDB and KairosDB, are built on Apache HBase and Apache
> Cassandra, respectively.
> >
> > However, many applications for time series data management have more
> requirements especially in industrial applications as follows:
> >
> >  * Supporting time series data which has high data frequency. For
> example, a turbine engine may generate 1000 points per second (i.e.,
> 1000Hz), while each CPU only reports 1 data points per 5 seconds in a data
> center monitoring application.
> >
> >  * Supporting scanning data multi-resolutionally. For example,
> aggregation operation is important for time series data.
> >
> >  * Supporting special queries for time series, such as pattern matching,
> time series segmentation, time-frequency transformation and frequency query.
> >
> >  * Supporting a large number of monitoring targets (i.e. time series).
> An excavator may report more than 1000 time series, for example, revolving
> speed of the motor-engine, the speed of the excavator, the accelerated
> speed, the temperature of the water tank and so on, while a CPU or an
> application monitor has much fewer time series.
> >
> >  * Optimization for out-of-order data points. In the industrial sector,
> it is common that equipment sends data using the UDP protocol rather than
> the TCP protocol. Sometimes, the network connect is unstable and parts of
> the data will be buffered for later sending.
> >
> >  * Supporting long-term storage. Historical data is precious for
> equipment manufacturers. Therefore, removing or unloading historical data
> is highly desired for most industrial applications. The database system
> must not only support fast retrieval of historical data, but also should
> guarantee that the historical data does not impact the processing speed for
> “hot” or current data.
> >
> >  * Supporting online transaction processing (OLTP) as well as complex
> analytics. It is obvious that supporting analyzing from the data files
> using Apache Spark/Apache Hadoop MapReduce directly is better than
> transforming data files to another file format for Big Data analytics.
> >
> >  * Flexible deployment either on premise or in the cloud.  IoTDB is as
> simple and can be deployed on a Raspberry Pi handling hundreds of time
> series. Meanwhile, the system can be also deployed in the cloud so that it
> supports tens of millions ingestions per second, OLTP queries in
> milliseconds, and analytics using Apache Spark/Apache Hadoop MapReduce.
> >
> >  * * (1) If users deploy IoTDB on a device, such as a Raspberry Pi, a
> wind turbine, or a meteorological station, the deployment of the chosen
> database is designed to be simple. A device may have hundreds of time
> series (but less than a thousand time series) and the database needs to
> handle them.
> >  * * (2) When deploying IoTDB in a data center, the computational
> resources (i.e., the hardware configuration of servers) is not a problem
> when compared to a Raspberry Pi. In this deployment, IoTDB can use more
> computation resources, and has the ability to handle more time seires
> (e.g., millions of time series).
> >
> > Based on these requirements, we developed IoTDB, a new data store system
> for managing time series data.
> >
> > IoTDB started as a Tsinghua University research project. IoTDB's
> developer community has also grown to include additional institutions, for
> example, universities (e.g., Fudan University), research labs (e.g, NEL-BDS
> lab), and corporations (e.g., K2Data, Tencent). Funding has been provided
> by various institutions including the National Natural Science Foundation
> of China, and industry sponsors, such as Lenovo and K2Data.
> >
> > == Rationale ==
> > Because there is no existed open-sourced time series databases covering
> all the above requirements, we developed IoTDB. As the system matures, we
> are seeking a long-term home for the project. We believe the Apache
> Software Foundation would be an ideal fit. Also joining Apache will help
> coordinate and improve the development effort of the growing number of
> organizations which contribute to IoTDB improving the diversity of our
> community.
> >
> > IoTDB contains multiple modules, which are classified into categories:
> >
> >  * '''TsFile Format''': TsFile is a new columnar file format.
> >  * '''Adaptor for Analytics and Visualization''': Integrating TsFile
> with Apache Hadoop HDFS, Apache Hadoop MapReduce and Apache Spark. Examples
> of integrating IoTDB with Apache Kafka, Apache Storm and Grafana are also
> provided.
> >  * '''IoTDB Engine''': An engine which consists of SQL parser, query
> plan generator, memtable, authentication and authorization,write ahead log
> (WAL), crash recovery, out-of-order data handler, and index for aggregation
> and pattern matching. The engine stores system data in TsFile format.
> >  * '''IoTDB JDBC''': An implementation of Java Database Connectivity
> (JDBC) for clients to connect to IoTDB using Java.
> >
> > === TsFile Format ===
> >
> > TsFile format is a columnar store, which is similar with Apache Parquet
> and Apache CarbonData. It has the concepts of Chunk Group, Column Chunk,
> Page and Footer. Comparing with Apache Parquet and Apache CarbonData, it is
> designed and optimized for time series:
> >
> > ==== Time Series Friendly Encoding ====
> > IoTDB currently supports run length encoding (RLE), delta-of-delta
> encoding, and Facebook's Gorilla encoding.
> >
> > Lossy encoding methods (e.g., Piecewise Linear Approximation (PLA) and
> time-frequency transformation are works-in-progress.
> >
> >
> > ==== Chunk Group ====
> > The data part of a TsFile consists of many Chunk Groups. Each Chunk
> Group stores the data of a device at a time interval.  A Chunk Group is
> similar to the row group in Apache Parquet, while there are some
> constraints of the time dimension:  For each device, the time intervals of
> different Chunk Groups are not overlapped and the latter Chunk Group always
> has a larger timestamp.
> >
> > Given a TsFile and a query with a time range filter, the query process
> can terminate scanning data once it reads data points whose timestamp
> reaches the time limit of the filter. We call the feature ''fast-return''
> and it makes the time range query in a TsFile very efficient.
> >
> >
> >
> > ==== Different Column Chunk Format (Unnecessary the Repetition (R) and
> Definition (D) Fields) ====
> >
> > While Apache Parquet and Apache CarbonData support complex data types,
> e.g., nested data and sparse columns, TsFile is exclusively designed for
> time series whose data model is \<device_id, series_id, timestamp, value\>.
> >
> > In a `Chunk Group`, each time series is a `Column Chunk`. Even though
> these time series belong to the same device, the data points in different
> time series are not aligned in the time dimension originally.
> >
> > For example, if you have a device with 2 sensors on the same data
> collection frequencies, sensor 1 may collect data at time 1521622662000
> while the other one collects data at time 1521622662001 (delta=1ms).
> Therefore, each Column Chunk has its timestamps and values, which is quite
> different from Apache Parquet and Apache CarbonData.  Because we store the
> time column along with each value column instead of making different chunks
> share the same time column for the sake of diverse data frequency for
> different time series, we do not store any null value on disk to align
> across time series. Besides, we do not need to attach  `repetition` (R) and
> `definition` (D) fields on each value. Therefore, the disk space is saved
> and the query latency is reduced (because we do not align data by
> calculating R and D fields).
> >
> >
> > ==== Domain Specific Information in Each Page ====
> > Similar to Apache Parquet and Apache CarbonData, a `Column Chunk`
> consists of several `Pages`, and each `Page` has a `Page header`. The `Page
> header` is a summary of the data in the page.
> >
> > Because TsFile is optimized for time series, the page header contains
> more domain specific information, such as the minimal and maximal value,
> the minimal and the maximal timestamp, the frequency and so on. TsFile can
> even store the histogram of values in the page header.
> >
> > This header information helps IoTDB in speeding up queries by skipping
> unnecessary pages.
> >
> >
> > === Adaptor for Analytics ===
> > The TsFile provides:
> >
> >  * InputFormat/OutputFormat interfaces for Reading/Writing data.
> >  * Deep integration with Apache Spark/Hadoop MapReduce including
> predicate push-down, column pruning, aggregation push down, etc. So users
> can use Apache Spark SQL/HiveQL to connect and query TsFiles.
> >
> >
> > === IoTDB Engine ===
> > The IoTDB engine is a database engine, which uses TsFile as its storage
> file format. The IoTDB Engine supports SQL-like query plus many useful
> functions:
> >
> >  * Tree-based time series schema
> >  * Log-Structured Merge (LSM)-based storage
> >  * Overflow file for out-of-order data
> >  * Scalable index framework
> >  * Special queries for time series
> >
> > ==== Tree-based Time Series Schema ====
> > IoTDB manages all the time series definitions using a tree structure. A
> path from the root of the tree to a leaf node represents a time series.
> Therefore, the unique id of a time series is a path, e.g.,
> `root.China.beijing.windFarm1.windTurbine1.speed`.
> >
> > This kind of schema can express `group by` naturally. For example,
> `root.China.beijing.windFarm1.*.speed` represents the speed of all the wind
> turbines in wind farm 1 in Beijing, China.
> >
> > ==== Log-Structured Merge (LSM)-based Storage ====
> > In a time series, the data points should be ordered by their timestamps.
> In IoTDB, we use Log-Structured Merge (LSM) based mechanism. Therefore, a
> part of the data is stored in memory first and can be called as `memtable`.
> At this time, if data points come out-of-order, we resort them in memory.
> When this part of data exceeds the configured memory limit, we flush it on
> disk as a `Chunk Group` into an unclosed TsFile.  Finally, a TsFile may
> contain several Chunk Groups, for reducing the number of small data files,
> which is helpful to reduce the I/O load of the storage system and reduces
> the execution time of a file-merge in LSM. Notice that the data is
> time-ordered in one Chunk Group on disk, and this layout is helpful for
> fast filtering in one Chunk Group for a query.
> >
> > Rule 1: In a TsFile, the Chunk Groups of one device are ordered by
> timestamp (Rule 1), and it is helpful for fast filtering among Chunk Groups
> for a query.
> >
> > Rule 2: When the size of the unclosed TsFile reaches the threshold
> defined in the configuration file, we close the file and generate a new one
> to store new arriving data spanning the entire data set. Like many systems
> which use LSM-based storage, we never modify a TsFile which has been closed
> except for the file-merge process (Rule 2).
> >
> > Rule 3: To reduce the number of TsFiles involved in a query process, we
> guarantee that the data points in different TsFiles are not overlapping on
> the time dimension after file mergence (Rule 3).
> >
> > ==== Overflow File for Out-of-order Data ====
> > When a part of data is flushed on disk (and will form a `Chunk Group` in
> a TsFile), the newly arriving data points whose timestamps are smaller than
> the largest timestamp in the Tsfile are `out-of-order`.
> >
> > To store the out-of-order data, we organize all the troublesome
> `out-of-order` data point insertions into a special TsFile, named
> `UnSequenceTsFile`. In an UnSequenceTsFile, the Chunk Groups of one device
> may be overlapping in the time dimension, which violates the Rule 1 and
> costs additional time compared to a normal TsFile for query filtering.
> >
> > There is another special operation: updating all the data points in a
> time range, e.g., `update all the speed values of device1 as 0 where the
> data time is in [1521622000000, 1521622662000]`. The operation is called
> when: (1) a sensor malfunctions and the database receives wrong data for a
> period; (2) we may want to reset all the records. Many NoSQL time series
> databases do not support such an operation. To support the operation in
> IoTDB, we use a tree-based structure, Treap, to store this part of
> operations and store them as `Overflow` files.
> >
> > Therefore, there are 3 kinds of data files: TsFiles, UnSequenceTsFiles
> and Overflow files.  TsFiles should store most of the data. The volume of
> UnSequenceTsFiles depends on the workload: if there are too many
> out-of-order and the time span of out-of-order is huge, the volume will be
> large. Overflow files handle fewest data operations but will depend on the
> use of the special operations.
> >
> > ==== LSM-tree ====
> > Normally, LSM-based storage engines merge data files level by level so
> that it looks like a tree structure. In this way, data is well organized.
> The disadvantage is that data will be read and written several times. If
> the tree has 4 levels, each data point will be rewritten at least 4 times.
> >
> > Currently, we do not merge all the TsFiles into one because (1) the
> number of TsFiles is kept lower than many LSM storage engines because a
> memtable is mapped to several Chunk Groups rather than a file; (2)
> different TsFiles are not overlapping with each other in the time dimension
> (because of Rule 3).
> >
> > As mentioned before,  TsFile supports ''fast-return'' to accelerate
> queries. However, UnSequenceTsFile and Overflow files do not allow this
> feature. The time spans of UnSequenceTsFile, Overflow file andTsFile may be
> overlapped, which leads to more files involved in the query process. To
> accelerate these queries, there is a merging process to reorganize files in
> the background. All the three kinds of files: TsFiles, UnSequenceTsFiles
> and Overflow files, are involved in the merging process. The merging
> process is implemented using multi-threading, while each thread is
> responsible for a series family.
> > After merging, only TsFiles are left. These files have non-overlapping
> time spans and support the ''fast-return'' feature.
> >
> > ==== Scalable Index Framework ====
> > We allow users to implement indexes for faster queries. We currently
> support an index for pattern matching query (KV-Match index, ICDE 2019).
> Another index for fast aggregation (PISA index, CIKM 2016) is a
> work-in-progress.
> >
> > ==== Special Queries ====
> > We currently support `group by time interval` aggregation queries and
> `Fill by` operations, which are similar to those of InfluxDB. Time series
> segmentation operations and frequency queries are work-in-progress.
> >
> > == Initial Goals ==
> > The initial goals are to be open sourced and to integrate with the
> Apache development process. Furthermore, we plan for incremental
> development, and releases along with the Apache guidelines.
> >
> > == Current Status ==
> > We have developed the system for more than 2 years. There are currently
> 13k lines of code, some of which are generated by Antlr3 and Thrift.  There
> are 230 issues which have been solved and more than 1500 commits.
> >
> > The system has been deployed in the staging environment of the State
> Grid Corporation of China to handle ~3 million time series (i.e, ~30,000
> power generation assembly * ~100 sensors) and an equipment service company
> in China managing ~2 million time series (i.e, ~20k devices * 100 sensors).
> The insertion speed reaches ~2 million points/second/node, which is faster
> than InfluxDB, OpenTSDB and Apache Cassandra in our environment.
> >
> > There are many new features in the works including those mentioned
> herein. We will add more analytics functions, improve the data file merge
> process, and finish the first released version of IoTDB.
> >
> > == Meritocracy ==
> > The IoTDB project operates on meritocratic principles. Developers who
> submit more code with higher quality earn more merit. We have used `Issues`
> and `Pull Requests` modules on Github for collecting users' suggestions and
> patches. Users who submit issues, pull requests, documents and help the
> community management are welcomed and encouraged to become committers.
> >
> > == Community ==
> >
> > The IoTDB project users communicate on Github (
> > https://github.com/thulab/tsfile) . Developers make the communication
> on a website which is similar with JIRA (Currently, only registered users
> can apply to access the project for communication, url:
> https://tower.im/projects/36de8571a0ff4833ae9d7f1c5c400c22/
> > ). We have also introduced IoTDB at many technical conferences. Next, we
> will build the mailing list for more convenience, broader communication and
> archived discussions.
> >
> > If IoTDB is accepted for incubation at the Apache Software Foundation,
> the primary goal is to build a larger community. We believe that IoTDB will
> become a key project for time series data management, and so, we will rely
> on a large community of users and developers.
> >
> > TODO: IoTDB is currently on a private Github repository (
> > https://github.com/thulab/iotdb), while its subproject TsFile (a file
> format for storing time series data) is open sourced on Github (
> https://github.com/thulab/tsfile
> > ).
> >
> > == Core Developers ==
> > IoTDB was initially developed by 2 dozen of students and teachers at
> Tsinghua University. Now, more and more developers have joined coming from
> other universities: Fudan University, Northwestern Polytechnical University
> and Harbin Institute of Technology in China.  Other developers come from
> business companies such as Lenovo and Microsoft. We will be working to
> bring more and more developers into the project making contributions to
> IoTDB.
> >
> > == Relationships with Other Apache Products ==
> > IoTDB requires some Apache products (Apache Thrift, commons,
> collections, httpclient).
> >
> > IoTDB-Spark-connector and IoTDB-Hadoop-connector have been developed for
> supporting analysing time series data by using Apache Spark and MapReduce.
> >
> > Overall, IoTDB is designed as an open architecture, and it can be
> integrated with many other systems in the future.
> >
> > As mentioned before, in the IoTDB project, we designed a new columnar
> file format, called TsFile, which is similar to Apache Parquet. However,
> the new file format is optimized for time series data.
> >
> >
> >
> > == Known Risks ==
> >
> > === Orphaned Products ===
> > Given the current level of investment in IoTDB, the risk of the project
> being abandoned is minimal. Time series data is more and more important and
> there are several constituents who are highly inspired to continue
> development. Tsinghua and NEL-BDS Lab relies on IoTDB as a platform for a
> large number of long-term research projects. We have deployed IoTDB in some
> company's staging environments for future applications.
> >
> > === Inexperience with Open Source ===
> > Students and researchers in Tsinghua University have been developing and
> using open source software for a long time. It is wonderful to be guided to
> join a formal open-source process for students. Some of our committers
> > have  experiences contributing to open source, for example:
> >
> >  * druid:
> >
> https://github.com/druid-io/druid/commit/f18cc5df97e5826c2dd8ffafba9fcb69d10a4d44
> >
> >  * druid:
> >
> https://github.com/druid-io/druid/commit/aa7aee53ce524b7887b218333166941654788794
> >
> >  * YCSB:
> > https://github.com/brianfrankcooper/YCSB/pull/776
> >
> >
> > Additionally, several ASF veterans and industry veterans have agreed to
> mentor the project and are listed in this proposal. The project will rely
> on their guidance and collective wisdom to quickly transition the entire
> team of initial committers towards practicing the Apache Way.
> >
> >
> > === Reliance on Salaried Developers ===
> > Most of current developers are students and researchers/professors in
> universities, and their researches focus on big data management and
> analytics. It is unlikely that they will change their research focus away
> from big data management.  We will work to ensure that the ability for the
> project to continuously be stewarded and to proceed forward independent of
> salaried developers is continued.
> >
> > === An Excessive Fascination with the Apache Brand ===
> > Most of the initial developers come from Tsinghua University with no
> intent to use the Apache brand for profit. We have no plans for making use
> of Apache brand in press releases nor posting billboards advertising
> acceptance of IoTDB into Apache Incubator.
> >
> >
> > == Initial Source ==
> > IoTDB's github address and some required dependencies:
> >
> >  * The storage file format:
> > https://github.com/thulab/tsfile
> >
> >  * Adaptor for Apache Hadoop MapReduce:
> > https://github.com/thulab/tsfile-hadoop-connector
> >
> >  * Adaptor for Apache Spark:
> > https://github.com/thulab/tsfile-spark-connector
> >
> >  * Adaptor for Grafana:
> > https://github.com/thulab/iotdb-grafana
> >
> >  * The database engine:
> > https://github.com/thulab/iotdb
> >  (private project up to now)
> >  * The client driver:
> > https://github.com/thulab/iotdb-jdbc
> >
> >
> >
> > === External Dependencies ===
> > To the best of our knowledge, all dependencies of IoTDB are distributed
> under Apache compatible licenses. Upon acceptance to the incubator, we
> would begin a thorough analysis of all transitive dependencies to verify
> this fact and introduce license checking into the build and release process.
> >
> > == Documentation ==
> >  * Documentation for TsFile:
> > https://github.com/thulab/tsfile/wiki
> >
> >  * Documentation for IoTDB and its JDBC:
> > http://tsfile.org/document
> >  (Chinese only. An English version is in progress.)
> >
> > == Required Resources ==
> > === Mailing Lists ===
> >  *
> > private@iotdb.incubator.apache.org
> >
> >  *
> > dev@iotdb.incubator.apache.org
> >
> >  *
> > commits@iotdb.incubator.apache.org
> >
> >
> > === Git Repositories ===
> >  *
> > https://git-wip-us.apache.org/repos/asf/incubator-iotdb.git
> >
> >
> > === Issue Tracking ===
> >  *  JIRA IoTDB (We currently use the issue management provided by Github
> to track issues.)
> >
> >
> > == Initial Committers ==
> > Tsinghua University, K2Data Company, Lenovo, Microsoft
> >
> > Jianmin Wang (jimwang at tsinghua dot edu dot cn )
> >
> > Xiangdong Huang (sainthxd at gmail dot com)
> >
> > Jun Yuan (richard_yuan16 at 163 dot com)
> >
> > Chen Wang ( wang_chen at tsinghua dot edu dot cn)
> >
> > Jialin Qiao (qjl16 at mails dot tsinghua dot edu dot cn)
> >
> > Jinrui Zhang (jinrzhan at microsoft dot com)
> >
> > Rong Kang (kr11 at mails dot tsinghua dot edu dot cn)
> >
> > Tian Jiang(jiangtia18 at mails dot tsinghua dot edu dot cn)
> >
> > Shuo Zhang (zhangshuo at k2data dot com dot cn)
> >
> > Lei Rui (rl18 at mails dot tsinghua dot edu dot cn)
> >
> > Rui Liu (liur17 at mails dot tsinghua dot edu dot cn)
> >
> > Kun Liu (liukun16 at mails dot tsinghua dot edu dot cn)
> >
> > Gaofei Cao (cgf16 at mails dot tsinghua dot edu dot cn)
> >
> > Xinyi Zhao (xyzhao16 at mails dot tsinghua dot edu dot cn)
> >
> > Dongfang Mao (maodf17 at mails dot tsinghua dot edu dot cn)
> >
> > Tianan Li(lta18 at mails dot tsinghua dot edu dot cn)
> >
> > Yue Su (suy18 at mails dot tsinghua dot edu dot cn)
> >
> > Hui Dai (daihui_iot at lenovo dot com, yuct_iot at lenovo dot com )
> >
> > == Sponsors ==
> > === Champion ===
> > Kevin A. McGrail (
> > kmcgrail@apache.org
> > )
> >
> > === Nominated Mentors ===
> > Justin Mclean (justin at classsoftware dot com)
> >
> > Christofer Dutz (christofer.dutz at c-ware dot de)
> >
> > Willem Jiang (willem.jiang at gmail dot com)
> >
> >
>
>

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