简介： 数据库是什么？未来的数据会被存在DNA里？数据库里的数据湖是什么？ 1月16日，扫地僧做了一场直播，请到我的同事——数据库资深专家封神，和来自帝国理工的高级讲师Thomas Heinis（托马斯·海尼斯），2人就数据库这个话题做了比较深入的探讨，老僧印象比较深的是一些前沿的DNA储存大数据等概念。在此老僧奉上双方谈话的全部内容，由于英国学者使用英文讲解，所以对全文进行了中英文的翻译。希望这个速记能帮助对前沿科学有兴趣的同好。
Moderator: Hello everyone. Welcome to the live streaming studio of the sweeper monks of Alibaba DAMO Academy. I am the assistant to the sweeper monks. This morning, our autonomous robot Xiaomanlv (Little Competent Donkey) took you guys on a tour in Alibaba Cloud Apsara Park without saying anything. I wonder if you felt anxious or not. Now let’s have a good chat.
Today, we have with us our old friend, the Sweeper Monk Fengshen.
封神（阿里云智能 云原生数据湖分析DLA 技术负责人)：大家好，我叫封神，来自阿里云数据库团队，09年加入阿里，目前主要做数据库数据湖分析方向，主要负责云原生数据湖分析DLA的技术，之前也做了10年左右的数据库与大数据相关的事。
Hello everyone, I am Fengshen from Alibaba Cloud database team. I joined Alibaba in 2009. Currently, I am mainly responsible for Data Lake Analytics, known as DLA. Before joining Alibaba, I had spent 10 years doing things related to databases and big data.
主持人：直播间还请到了1位来自远方的客人，Mr. Thomas Heinis，他是帝国理工学院的讲师，请客人自我介绍。
Moderator: We are also honored to have with us here Mr. Thomas Heinis, lecturer at Imperial College London. Mr. Heinis, please kindly introduce yourself.
Yes, sure. My name is Thomas Heinis. I am a senior lecturer at the moment at the Imperial College in London. I do research here in the research group, which basically takes care of everything to do with data.So I do a lot of data analytics, also, data visualization, and then also data storage at the moment, including all new technologies such that we can basically analyze data efficiently and understand it in the future.
Moderator: Since we have invited two experts in the field of databases, the topic of our discussion today is certainly inseparable from databases. For non-professionals like us, probably the most common words we have heard is dropping a database. And our most intuitive understanding of databases is Excel tables. First, please introduce to our audience what a database is. Mr. Heinis, how would you explain databases to freshmen in your first class?
I usually explain a little bit of history about it. That's all to do with banks. Banks had a lot of data in the 60s and 70s. And they needed to organize that data. That's kind of where relational databases come from.
Essentially, what I tell students is that a database is a collection of lots of information, lots of data that are organized so that it can be analyzed, accessed, managed, updated very efficiently, right?
So computer databases, typically from the navigation of data files. Databases, traditionally, or historically, do come from banks. There was a lot about bank customers and clients their balance of their accounts and information.
And within databases, then we have this massive for this big branch of this big technology called relational database, which is really where we organize data to tables, rows, columns, which really contain information about customers, clients, transactions, sales, etc. and all kinds of very well structured information.
And some of the students have already, on the start, already heard about SQL, which is the query language to ask for a query database.
In recent years, things have changed a little bit. So relational databases, they do come from the 60s 70s driven by this banking use case, for this banking application.
And in the last 20 years, things have changed drastically, right?
Because we have new applications that need to organize the data such as scientific applications, or the types of applications like social networks, and etc.
Basically, these applications require slightly different databases. And so that's where then we went to kind of noSQL databases or non-relational databases and we moved to different use cases. And we also move to managing much much more data, What is nowadays called Big Data. Basically, we collect tremendous amounts of data. And then we need to come to databases to analyze and store these data.
Moderator: I have a question to Fengshen. Why are databases more and more important in the modern era?
Actually, databases have always been important. To put it simply, data cannot be lost. If an enterprise loses its core database, it might directly go bankrupt.
Why are databases more and more important? That is because data also contain treasure, In the past, data were just stored there. Generally, core data were stored, such as the data of transactions, customers, and goods. Some log data were stored just for query purposes. There were no buried points, let alone crawling or purchasing data.
The Internet has gone through several stages, from the news portal era in the beginning, to the era of interactions with such platforms as BBS and Taobao, to the era of wireless technology, and then to the era of intelligence. More and more data have been generated in the process of evolution.
-According to the statistics released by IDC, the amount of global data generated in 2005 were 130 EB, and that in 2019 was 41ZB, an increase of 322 times.
-From the perspective of data application, more and more companies have also made outstanding achievements with data. For example, the Top 100 well-known internet enterprises, including Alibaba, Toutiao, Baidu and DiDi, are all data-driven enterprises. In terms of the traditional industries, data technology is used in smart parks, city brain, agriculture brain, smart agriculture, smart cities, smart medicine and Industry 4.0 to empower each product and help the respective industries achieve digital transformation and increased efficiency. Seen from the perspective of industrial application, it is necessary to give full play to imagination and use data to empower industrial transformation and growth.
-Seen from the domestic situation, the Chinese government has also put forward the concept of new infrastructure. Its core technologies include cloud computing, big data, artificial intelligence, 5G and blockchain. And the core of these core technologies is the application of data. It is expected that in 10 years, we will enter the era of the Internet of Everything, when the growth of data volume will be even faster. Relevant institutions have found through research that the COVID-19 pandemic has expediated the digital transformation by 5 years.
-From the perspective of universities and research institutions, big data technology and artificial intelligence programs have been widely offered these days. In short, in addition to talents in the 21st century, what else is the most expensive? The answer is data. Data are equivalent to the oil of the 20th century. Data are the lubricant for the functioning of the whole society in the 21st Century. Due to the increasing importance of data, databases are also becoming more and more important. And databases are the core carriers of everything.
Questions: What is the difference in the definition of big data between China and the U.K.? Will such differences lead to different perceptions of databases between the programmers in the two countries?
封神：大数据其实我认为没有准确的定义。比如，如果数据量比较小，但是训练用的机器比较多，也可以认为是用到了大数据技术。我一般认为用数据驱动业务发展就属于使用了大数据相关的技术。之前国内提Big Data（中文指：大数据）比较多，现在国外提Data Lake（中文指：数据湖）的概念比较多，主要还是云公司在主导，数据很多存在了对象存储上。阿里数据库团队提的是库、仓(Data Warehouse)、湖(DataLake)、多模（Multi-Model），并且我们还专门做了一个 云原生数据湖分析DLA的产品。另外，我们看到著名的咨询公司Gartner把大数据报告合并到了数据库报告里面。
Fengshen: I don’t think there is a precise definition of big data. For example, if the amount of data is small, but many machines are used for training, it can still be considered that big data technology has been used. I generally think that adopting the data-driven business mode is equivalent to using big data-related technologies. In the past, the concept of Big Data was mentioned a lot in China. Nowadays, the concept of Data Lake becomes popular in foreign countries. In most cases, cloud companies are taking the lead. And a lot of data exist in object-based storage. Alibaba’s database team mentions data library, Data Warehouse, Data Lake, and multi-model. We have also specifically made a product based on Data Lake Analytics (DLA). In addition, the renowned advisory company Gartner Inc. integrated big data reports into its database report.
In my view, databases include traditional database technologies (such as MySQL and PG) as well as data warehouse and data lake technologies, such as open-source Spark, Hadoop, Alibaba’s ADB and DLA. They also include the Lakehouse technology which has been quite popular these days.
I haven’t had much communication with programmers in the UK. But I have had some communication with programmers in North America. Overall, our perceptions are more or less similar. The speed of technology communication is also relatively fast. So, the understanding should be more or less similar. Besides, the huge market in China and the larger amount of data generated here will accelerate the development of data application.
托马斯·海尼斯：On some level, yes. I think this is gonna be long long rounds. But I don't think you know, if you look at it, from a technical perspective, the types of data could be the same, it's going to be very similar. But what I do think is that the scale is massively different. And that's quite, you know, just because there's so much more data available in China.
And it's because I think personally, I think it's because the society is a little bit more technologically advanced, or is it easier to…I think China adopts technology easier, which means that, for example, you have more sensors everywhere with traffic measurements, and an engine mentioned the DiDi, right, which, which, you know, produces the same kind of data as Uber, for example, but on a different scale, right. And that applies to everything.
And what he said about the pandemic, being a catalyst for this transformation is absolutely also absolutely true. Like we have made way more electronic payments, now, we have just everything is more digital, and I think a lot of it will stay digital, and that produces data that produces data that we need to analyze.
So with what I mentioned, about China, being a technological, a bit more about having more technical technological affinity means that we basically have more places where we collect data, and much more sensors, people interacting online that produces more data.
And then also, you know, China's population is huge. So that also means that more data is being produced. So it's, I would say, you know, there are two technical challenges when it comes to big data or databases in general.
One is the data formats, you know, that's life changing, as mentioned, is going towards also data lake right loads of different formats. But I don't think that differs much between China and the rest of the world.
But what what is really different is the amounts of data. And so that means that we need whatever we develop the analysis, visualization, storing the data that needs to happen efficiently on a much, much larger scale, which then again, brings in a lot of technical challenges and challenges as well. Right. So I think i think that's that's, that's what I think that's the difference. But I think the definition as such, it's roughly roughly the same.
It's also I also have the feeling That, that, you know, China has a different, the Chinese people have a different understanding of personal data as well,
it's very, very difficult for us to get data from companies here or from you know, there's always a perception of these breaches data privacy, whereas in China and appear of works a lot with with Chinese University as well. So we get a lot of data from I don't think it's DiDi, but somebody saw some similar data sets, it's quite easy. So there's also kind of like, I feel like China has really this, this kind of this, this more affinity towards technology, and this is a and like, this more of a project, let's see what we can do with the data. Let's see if we can improve things, you know, so we're also collaborating on a traffic optimization project that you know, they collect massive amounts of data about which vehicle passes through the road, where at what speed what's the congestion level? Can we remove traffic and all these kinds- of things? It's really kind of like a very pragmatic approach to using data really, what can we do to improve you know, everything really. That’s what data does these days.
With the emergence of the concept of big data, how has databases evolved over the years? Fengshen, please share with us your view on this question.
To figure out how databases have evolved, let’s first look at how they came into being. In a broad sense, data recording dates back to a long time ago. Back 5000 years ago, humans began to count with knots; 2000 years ago, paper started to be used. And in 1946, the first computer was invented. After the invention of the first computer, the databases in the modern sense came into being. Let me use an analogy to explain what a database is. Suppose you have a housekeeper, and the housekeeper has a ledger. You inform the housekeeper how much you spend and how much you earn every day. The housekeeper records your income and expenditure in the ledger accordingly. You can then know how much money you currently have and how much you spend each month. In this case, the database is the housekeeper + the ledger: The housekeeper provides the computing power, while the ledger provides the storage.
The development of databases has gone through several stages. And in order to “keep good accounts”, databases have also been evolving. We generally divide the development of databases into 4 stages:
The two decades from 1970 to 1990 was an era of business databases when fees were charged for the use of databases.
1990~2000 开源数据库时代 开源时代
The decade from 1990 to 2000 was an era of open-source databases.
2000~2015 互联网浪潮 大数据时代（大数据计算、存储、NoSQL）
The period from 2000 to 2015 was an era of Internet and big data (big data computing, storage, NoSQL)
2015~现在 云的浪潮 云原生时代+AI
Finally, the era from 2015 to the present is an era of cloud technology and the widespread application of Cloud Native and AI technologies.
数据库的发展跟几个因素有关， 硬件的发展，需求； 硬件主要指 存储、网络、内存、CPU。存储就是存数据，内存与CPU关系到计算力，网络就是传输。
The development of databases is related to several factors, including the development of hardware and the market demand; hardware mainly refers to storage, network, memory and CPU; storage refers to data storage; memory and CPU are related to computing power; and network concerns transmission.
大数据这个词语大概在10年前开始流行，大数据系统开始独立于数据库系统发展的，随着最近5年的发展，大数据相关技术又慢慢与数据库技术结合回归到数据库的大家庭。比如，2020年，著名的咨询公司Gartner把大数据报告合并到了数据库报告里面。最为典型的是 DalaLake的发展，融入了事务&MVCC的概念，NewSQL的发展，NewSQL也融合了分布式的理论，并且还有一个HTAP的方向在探索。目前数据库领域分为TP、NoSQL、AP等领域。TP一般有单机、分布式、事务型的数据库；NoSQL就相对散一些：宽表、图、文档、时序、时空等；AP有Data Warehouse、DalaLake领域。
The term big data became popular around 10 years ago, when big data systems started to develop independent from database systems. With the development in the last 5 years, big data-related technologies have slowly returned to the database family by combining with database technologies. For example, in 2020, the famous research and advisory company Gartner integrated its big data report into its database report. And the most typical case is the development of Alibaba’s Dala Lake Analytics team, which incorporates the concepts of transactional databases & Multi-Version Concurrency Control (MVCC). Besides, the development of NewSQL also incorporates the theory of distributed databases. And the direction of HTAP is under exploration. Currently, the database field consists of such segments as TP, NoSQL and AP, TP generally consists of standalone databases, distributed databases and transactional databases; NoSQL covers a relatively wider scope, including wide tables, graphs, documents, time sequence and space-time. AP consists of such fields as data warehouses and data lakes.
The prospects of the business community in the next 5 years are definitely foreseeable. In the next 5 years, the core development directions of the database field are Cloud Native and distributed databases, which specifically include serverless, integration of databases and big data, intelligence, security and trustworthiness, hardware and software integration, offline and online integration, and multi-mode data processing. For example, I am currently responsible for Data Lake Analytics (DLA), which can be considered the upgrade of the traditional technologies such as big data, Hadoop and Spark. It requires the integration of traditional database technologies and is based on the Cloud Native architecture of complete separation of storage and computing. It selects object-based storage and supports the archiving of common messages as well as the data in TP & NoSQL databases systems. Normally we archive the data in the data lakes. Besides, DLA also supports transactions and versions. Besides, the Spark and Presto components are also incorporated to achieve elasticity for the Cloud Native, which is accessible at any time and charged on the basis of demand. The loss of bandwidth after separation is solved by introducing local Cache.
Well, there's not much more to add to what Fengshen has already said. Right. But I think I think what would what would I do want to maybe add really is that also, you know, Like you said absolutely correctly, is that open source databases have done a tremendous service to the community in kind of getting databases everywhere. I will say that I think it's also kind of the databases that have become much, much, much easier to use as well. And, you know, years back the first year students, they'd never seen a database. Today, if I asked, they've all seen MongoDB, or other technologies, kind of like easy-to-use databases, you know, not relational databases necessarily, but easy to use technology.
And that really has made a difference in terms of training people, they also kind of has changed their understanding the approach of people to using databases. Now, back in the day, people were storing data just in RAW files. Nowadays, they know, if I want to have efficient access to the data, then I need to use a database and they know how to do so.
So databases have really changed, or kind of database have become much more pervasive. They're used everywhere these days. So that has definitely changed.
And now I unfortunately, forgot your question, which I didn't answer.
So essentially, what really changed, right, and I said this initially, already kind of databases were designed relational databases were designed for banking applications that were revolving around transactions, which was really the centerpiece of banking applications.And that has made a lot of design decisions difficult.
And then in recent years, like XX had mentioned, right, databases are kind of new use cases emerged, all of a sudden, we no longer have the data we have along fits nicely in a in a table, we actually have a graph.
So we have graph databases, or we realized a lot of data is natively very structured in a document, XML or similar. So we develop document databases.
So there's, there's the now we, in the early, maybe around 2000, a little bit after 2000, we had this understanding that one size doesn't fit all. So we need to have different types of databases. So I mentioned graph database, document databases. But there have also been other other databases, very customized databases. For scientific applications, right?
They produce massive amounts of data, like physics experiments, like astronomy, DNA experiments in biological experiments, they have all kinds of their own database technology these days,
Back in the day, we've tried to fit everything in relational database and it didn't work really well. So each one of those now as their own title type of database.
At the same time, we also, you know, more and more data has been produced in different formats. And this is really where the kind of what is this notion of a data lake of engine has been mentioning is coming from that we have tons of data in different formats, we still want to analyze the data as a whole. So we need some sort of kind of some sort of integration between that or some sort of way of analyzing heterogeneous different data types. And that has also changed. So we have now this capability to just produce data, throw it in a database, Put simply, and then analyze it efficient, efficiently, efficiently at scale. Right. So that's really how things I believe have changed.
There's also other trends like cloud computing, in general, which has made it which is also supported for particularly smaller businesses to have their own database their own data solution. Because they no longer need to own the resources. And they can just if they have a big analysis to run on their data, or they just use cloud resources to do so temporarily, without having the hassle of owning. Right. So that has also held,
then we also have a huge trend in terms of hardware. So we have, obviously we have better hardware. And every now and again, the database community tries to really optimize the database for new hardware beat is multi core processors, which are not particularly new, but all kinds of hardware aspects of new CPUs, new types of memory, non volatile memory, for example, change a little bit how we organize and analyze data. So a lot of hardware trends has also changed or shaped database, database technology.
And then finally, what has happened in the last couple of years is really the use of machine learning or artificial intelligence in and around data and that has driven a lot of research and has also produced a lot of products. And when I talk about AI or artificial intelligence databases, it's really kind of The database research community has taken an approach And has done a lot of different things
for example, you know, artificial intelligence machine learning requires a lot of learning, which requires a lot of data, and for that we need to have data that is clean and has been processed and has been manual has been brought in the right format. So, that's a database task.
And then the learning itself is also to some degree a database. Right And so, we have worked on that, that has had a tremendous impact in recent years.
We also use artificial intelligence within the database itself to accelerate the database accelerate query execution the analysis. And then we also use artificial intelligence to organize the database itself.
so that so I would say that artificial intelligence is a mega trend of course, we all know and you know has touched all aspects of life but it is also interesting enough to touch databases which not just touched but changed profoundly how we design and use databases.
Our two teachers just shared a lot of basics about databases with us. If I were to give this livestreaming interview a name, I would call it “Database Essentials”. Just kidding. This is actually a live interview about cutting-edge database technologies. In fact, any discipline exists in two different forms, one in academia and the other in industry. It is important to apply technologies in industry. You can find many application scenarios for a discipline in industry, such as Double 11 Shopping Festival and Industrial Brain mentioned by Fengshen just now. In comparison, the exploration in academia is often very imaginative. We would love to ask you two to look ahead into the development of databases in the future. For example, what will databases be like in 5 years, 10 years, 50 years, or even 100 years?
I would like to talk about some of the directions I focus on. In the next 5 years, the core development directions of databases would be Cloud Native and distributed databases. Specifically, I’m talking about serverless, integration of databases and big data, intelligence, security and trustworthiness, software and hardware integration, offline and online integration, and multi-mode data processing. These technologies will have an impact on each subfield of each database. As to database research in academia, I only have a vague idea. Seen from the history of human development, the development of databases should be faster and faster. However, computers nowadays are still based on the von Neumann architecture. I have no idea when it will be replaced. And I actually have no idea what kind of development will have happened in 10 years. At present, the only thing I am sure about is to maintain a sense of awe and keep learning.
主持人：学术界就是Mr.Heinis的研究方向了，请 Mr.Heinis 继续来说
托马斯·海尼斯：Yeah, well, what's the future? It’s difficult to predict, right？But in terms of, you know, kind of like a five year perspective, the only thing I would add in that I think will make a difference in the in the short term is probably also, like I mentioned, AI, artificial intelligence helping us a little bit to organize the data to accelerate analysis, etc.
So I think we're kind of lucky that this is the case. Because a lot of students want to work on AI. And if you can kind of combine as a database technology, we get a lot of talented students involved. But yeah, so I think in the short term, I think AI will also have an impact on databases.
I think also that visualization will become important. And we move there to virtual reality, right, kind of which, which offers us a much more, much more kind of, you know,
we can kind of interact with the data, we can touch the data to some degree, you know,
I used to do research and have a feed with gloves with haptic feedback, we can touch to data, this kind of thing will I think will become more important not for an individual analyzing data. But I think for collaborative analysis of data to analyze data together to understand it together, I think that's where we also need to put in and put some research to kind of like help to, to find easier ways for people to understand the impact of things.
And then, like Fengshen said right, at one, one important thing that's going to happen fairly soon, probably five to 10 years, maybe a little bit more, it's going to be quantum
and quantum. You know, it's difficult to fathom what is gonna, what it's going to do to our two databases. But one thing is for sure, I believe with quantum sensing, quantum sensors will just have so much more data to deal with. And that will challenge database technology, or Big Data technology in itself, right?
Then when it comes to go a little bit beyond 20-30-50 years, maybe or 50 years a bit later. But yeah, one of my favorite topics DNA storage basic for the store information to store data within synthetic DNA. And this is interesting, because we know essentially has been talking about numbers initially how much data we have
a lot of this data we don't look at every day, right, we store it in the long from the long term, because we need to for the law says we have to keep records around for hundreds of years, right.
And we do this with traditional technology with tape, disk, they don't last forever, the last maybe 10,15 years. And then we need to copy the data on to a new disk or a new tape etc. So as always, as data migration, as much as the hassling is also quite expensive. And a lot of companies don't want to afford this anymore, can't afford to do this anymore.
So what we're looking at with DNA storage, for example, is really to store data for 10s of years, maybe hundreds of years, right, such that we can retrieve it.
So we really can take the data, convert it to two strings of nucleotides and then synthesize this and store it in, in the fridge essentially. And when we need it, we sequence and get it back. So anyway, that's kind of I think that's going to happen.
So generally, I don't want to focus too much on DNA storage itself, I think like, the underlying technology will change drastically
in the past, we looked a lot of when we looked at storage, the storage medium, we had a lot of collaboration with computing, and electrical engineering. Now I think we're getting to a point where we go from, from computing, collaborating between computing and biology, or chemistry, etc.
Doesn't have to be DNA can be another kind of storage medium. But I think that's what's going on.
And what's quite interesting there is also I think, when we look at a little bit beyond 20 years, when it comes to DNA storage, but we can also implement some of some data processing some data analytics on top of the DNA using biological processes,
which is extremely energy efficient, and also very, very fast.
There are limits to this technology, but we'll find out over the next couple of years. next couple of decades, maybe we'll come in on there
but I think generally that we also will the whole field of computing will expand into other into other will collaborate more with other fields. And that also has implications for databases for data analytics to
can use biological processes or chemical processes or anything or similar to do computations right. I think that's that's what's gonna that's what's definitely gonna happen. But it's, you know, the difficulty in the future is very difficult to predict.
The implication of quantum, for example, like I mentioned, quantum sensing will deliver tons of data. But there's gotta be other implications.
For example, it's one tiny operation in a database, query optimization, which is kind of like you give the database a query, it figures out how to do it efficiently. And that takes a lot of time to compute to figure out how to execute that query efficiently.
And we've also already seen in the community that somebody took a query optimization and implemented it on a quantum computer showing that this would be massively faster to optimize the query on the quantum computer. So there's a lot of really I don't think I understand all the implications of quantum but there the quantum computing but that will be definitely also have an impact on databases.
So in the short term, adding to essentially I say, is really kind of I think AI is having a tremendous impact in the short term, in the somewhat longer term, I think, we really have to think about interfaces to data virtual reality being one of them, right?Augmented reality being another, but we need to think about how can we make it easy for people to interact with data and understanding typing the query that works for an analyst that's not going to work for everyone right for pretty good for for a broad class of people who need to you know, I think we all need to deal with interpret and analyze data and I think we need to make it easy for everyone. That's a little bit more medium term and in the long term, I think that hardware will change dramatically with quantum with DNA storage with other types of storage medium etc. But 100 years I'm not gonna make a prediction here that's too far out.
Databases research falls within the expertise of Mr. Heinis. Let’s invite Mr. Heinis to share with us the prospects of database research in academia. I would like to thank our two friends. I’d like to take this opportunity to share a recruitment ad for the database team and Dr. Heinis.
托马斯·海尼斯：Currently, the specific sub-fields of databases, including TP, NoSQL and AP are all recruiting talents. We welcome those who are passionate about database technology, who have technical aspirations, and who are technically proficient. Currently I focus on DLA at Alibaba Cloud. Look forward to hearing from you. Fengshen: firstname.lastname@example.org.
Absolutely, of course I do. We have Imperial has been very good collaborations with China in general. And we, for some reason, I don't know why. But we have a big share of Chinese students though, and they are very talented. So if any one of those would like to work on kind of and we offer everything, you know, internships, student shapes for PhD and postdocs as well, if anybody wants to work to to change database technology in the future, of course, you can go to Alibaba or you can come You know, seriously, I'm really looking always recruiting interested to the students, we look at all kinds of aspects of databases, like I mentioned. So, one of the some of the topics that I work on with my team are AI, virtual reality and DNA storage, but we also have other aspects. So, if anybody wants to kind of you know, learn learn these technologies work with these technologies and contribute to this research please do get in touch.