Today's companies have to handle a zoo of data of diferent models. Multi-model databases promise to simplify data administration for the parallel usage of diferent data models. Compared to the other data models, semantic data models introduce an additional abstraction layer for reasoning purposes, such that semantic data models provide superior capabilities. Hence semantic multi-model databases use the semantic data model as main glue between the diferent data models. Furthermore, applications as well as databases are today running on diferent platforms like mobile devices, web, desktops, servers, clouds and post-clouds (e.g., fog and edge computing). Hybrid multi-model multi-platform (HM3P) databases and its semantic counterpart (SHM3P databases) integrate the diferent platforms in order to ofer their advantages and benefits for data distribution, query processing and transaction handling to their users. In this paper we introduce and discuss the novel concept of SHM3P databases and its open challenges.
model data [3] hindering optimizations down to the
physical layer of connected DBMSs [
forms, but we are considering the database server here.
Single instance of SHM3P Database offers (fully cross-platform optimized) functionality of & replaces
GPU
Quantum DB Quantum Computer
Cloud DB Cloud Reasoning: Lightweight reasoning on large data sizes of IoT devices
IoT DB MemoMryaDinBOn the Edge GPU-accelerated
Parallel Server Mobile DB Mobile Devices & Infrastructure Heavyweight reasoning on moderate data sizes
Heavyweight reasoning on large data sizes
Reasoning on small data sizes of mobile devices
How to integrate the different reasoning capabilities and requirements into one transparent global reasoner? (SHM3P) DBMS supports a (global) semantic layer (for cerning MM-DBMSs, multi-platform development, querying and reasoning purposes) over all platforms of databases running on diferent platforms, polyglot peran HM3P DBMS. sistence and further related work. Section 3 introduces SHM3P DBMSs and explores the advantages, and analyses envisioned platforms and common properties of their combinations. Finally we summarize the results and provide an overview of future work in Section 4.
Whereas today’s M3P DBMSs are typically developed for platforms of the same type (like windows and linux servers, see Section 2.1), some other even span over a (locally installed) private cloud and a public cloud (in a so called hybrid cloud2). In contrast, we envision SHM3P DBMSs over platforms of diferent type 2. Basics (like IoT and hardware-accelerated parallel servers) integrating the features of databases developed for these 2.1. Databases for Multi-Model Data platforms (like energy-savings on IoT devices and high Polyglot persistence uses diferent databases supportthroughput on servers) while ofering advanced global ing diferent data models (and maybe running on difreasoning capabilities over all platforms. Hence SHM3P ferent platforms) within one application [3]. Federated databases support any data model at any platform by query languages enable polyglot persistence by suptightly integrating them with a semantic layer. For an porting queries over heterogeneous data stores within example installation, see Figure 1. one single query. One example of such a query lan
Our main contributions are: guage is CloudMdsQL [6], with which one can for• the introduction of SHM3P DBMS as new type of mulate queries over SQL and NoSQL databases. The
DBMS, proposed prototype even optimizes the queries
glob• a detailed discussion of the current state of the art ally and pushes operations down to the integrated SQL
about and comparative analysis of DBMS designed and NoSQL databases as much as possible. A similar
for diferent platforms with special attention to Se- approach is taken by [
same type.
Drill optimizes a query plan to leverage the datastore’s where most of which, i.e. 4 of these 5 MM DBMS with internal processing capabilities and by considering data RDF support, also manage graph data. The graph model locality. Commercial multi-store products like IBM seems to be more popular (12 from 21 MM DBMS). BigInsights, Microsoft HDInsight and Oracle Bigdata MM DBMS with RDF support typically don’t support Appliance as well as open source projects like PrestoDB4 reasoning at all or only in a rudimentary way, such integrate diverse data sources by using database con- that users should look for native semantic DBMS if nectors (like JDBC drivers). Tatooine [8] uses a se- reasoning is needed. Hence reasoning seems to be chalmantic layer as glue between databases for diferent lenging in the MM DBMS context. Most multi-model data models supporting a semantic integration. How- databases run SQL, SQL-like or extensions of SQL ever, all these polystores also don’t support to fully op- queries. Binaries of these databases are ofered in matimize queries across the integrated, but independent chine code (often compiled from C/C++) or for the Java data sources, which limit data processing. virtual machine (JVM). They usually run on all or a big
Federation Databases [9] and multidatabases [10] subset of the major desktop operating systems linux, place a mediator between diferent autonomous windows, macOS, unix and their variants. Few multidatabases for integration purposes by reformulating model databases like IBM DB2 run on mainframes opqueries according to a global schema to the native erating e.g. z/OS. While all ofer to run in the cloud, schemes of the integrated databases, which afterwards some are also enabled for the hybrid cloud. In the execute these queries. Today, some research focus on hybrid cloud, a (locally installed) private cloud is tofederating databases following the polyglot persistence gether used with a public cloud. Hybrid clouds deapproach: For example, DBMS+ [11] provides unified crease costs spent to the public cloud provider while declarative processing for the integration of several still having on-demand resources with the illusion of processing and database platforms. BigDAWG [12] of- infinite capacity at the public cloud for a surprising fers location transparency while running queries high resource demand. against the three diferent integrated systems While all multi-model databases run on diferent platPostgreSQL, SciDB and Accumulo. forms, they don’t integrate database instances on dif
Multi-Model Databases: A multi-model database ferent types of platforms and diferent types of databases.
is one single database for multiple data models, which Databases in hybrid clouds combining the resources of
fully integrates a backend to ofer advanced perfor- a locally installed private cloud with a public cloud are
mance, scalability and fault tolerance [
Many DBMSs are implemented in C/C++ for per- server and desktop computers, there exist only few
apformance reasons and run in native machine code for proaches for the cloud and for P2P networks. There
operating systems like Windows, Linux, Unix and Ma- exist only few approaches for trigger and continuous
cOS (see [
Multi-Platform DBMSs are typically either imple- situation is quite comfortable for the Semantic Web:
mented in C/C++ or in Java. Ports are often available The W3C standardized the protocol to query SPARQL
for Windows, Linux, Unix (sometimes for Solaris) and endpoints in [17]. The protocol [17] is widely
supMacOS (see [
Size: Data: Company: Devices: Binary: 21 tyeB 210iiKb 220iebM230iibG 240iTeb 250iePb 260ixEb 270iZeb 280iYbo 2167 faedrveannttaapgpelsicbaetcioanussecetnhaeyriohsa,vdeevbieceens,dpervoepleorpteiedsfoofrtdhiefi-r Decimal: 10 103 106 109 1012 1015 1018 1021 1024 1050 indexed data (velocity, heterogeneity, size etc.) and tyeB ilKo gaeM igaG raTe taeP xaE ttZae ttaYo trEahsdoatoabna.seTsa.bDleat1abcaosnetsaihnasvea traoiulogrhedevthael uiratairocnhiotefctthuersees Office Internet Big Data* noaccording to the properties of the diferent platforms,
IoT smbut often also to the required properties coming from SMEs Global Player toAtheir applications. Especially distributed databases can
not ofer all: The well known PAC theorem [19] deIoT Device Cluster Multi- scribes trade-ofs, where developers of distributed sys
Cloud Cloud tems (and hence also distributed databases) can choose
Server to fully support only two features with high eficiency
Desktop out of three: Partition-tolerance, Availability and MaCineMnteramlizoerdy Hardware CloIuodT rCeocntlsyisatlesnociyn.thFeocraesxeaomfnpeletw,iofrtkhpeasrtyistitoenms awnodriksshcigohr-ly
Mobile Web Cloud available, then consistency must be relaxed, such that
Desktop Cloud some replicas may contain older states and not the Web/Mobile Fog/Edge/Dew most recent ones. The PACELC theorem [20] refines P2P the PAC theorem and states that in the case of network
Partitions only Availability or Consistency is
guaranS*MEs: sSomcaialll amneddmiae,dseiuamrc-hsiezendgiennetserprises teed. In case of no failures when the databases run
normally (Else), then there is a trade-of between Latency
and Consistency, i.e., only small latency or high
consistency can be guaranteed, but not both at the same
time. Distributed triple stores, which are built on top
of NoSQL databases, inherit the properties of their
underlying systems: For example, D-SPARQ [21]
supports PA/EC, because it is based on MongoDB7.
JenaHBase [22], H2RDF [23] and H2RDF+ [
Hence there is a need to run these diferent types of
databases at the same time, but there might be also
the need for integrating the data of these databases
(like in the scenario of combining the data of IoT
devices with accounting data). For an advanced
processing of this diferent types of data stored in diferent
databases and other database tasks it is indispensable
to break the boundaries of single installations of these
DBMSs and to run one single DBMS. Furthermore, it
is desirable that this single DBMS provides a
semantic layer for advanced processing and reasoning
capabilities and for a tight integration of the diferent data
models. This would also allow to ofer the best features
Figure 2 provides an overview over data sizes of
diferent types of data used in companies, devices, databases
and platforms. It already becomes obvious that some
types of databases fit better to the considered types
of data and company, used devices and platforms than
the others. Hence the diferent types of data are stored
on and processed at diferent platforms dependent on
their size, the devices they are generated at and other
properties like their velocity. Integrating these data
sets implies to support multiple models and also
different platforms at the same time. This also requires to
support and integrate diferent types of databases
running on diferent platforms. For example, one might
combine the data of IoT devices (stored in an IoT
database running on the edge of the network) with
the accounting data containing the remaining time for
charging of (stored in a main memory database
running on an employee’s desktop computer). These
different types of databases have diferent properties and
7https://www.mongodb.com/ (accessed on 17.12.2020)
8https://hbase.apache.org/ (accessed on 17.12.2020)
9https://github.com/CM-Well/CM-Well (accessed
17.12.2020)
10https://cassandra.apache.org/ (accessed on 17.12.2020)
on
of the diferent types of databases to applications and ecution plans are ideal for many-core CPUs and GPUs
users “under one hood” transparently or with an in- as well as whenever the best possibilities among
enutelligent integration into one query language and API. merated ones must be found (like in query
optimizaThis single SHM3P DBMS installation runs over all tion and multi-version concurrency control (MVCC)).
platforms at the same time ofering the advantages of Complex operations like joins processing large data
all the diferent types of DBMSs (to the data that has inputs are very suitable for GPU-acceleration, too (see
been previously processed by the single installations) e.g. [
Server Platforms are typical platforms for database binatorial) optimization problems, which is e.g. the servers of small to medium-sized enterprises (SMEs). case for trafic control 12, selecting the execution plan The DBMSs running on servers are usually centralized with the best estimated costs (from a set of enumerdatabases, which are operating in parallel on multi- ated plans) [27], concurrency control between transaccore and sometimes many-core systems, often in vir- tions [28] as well as optimizing transaction schedules tual machines. Relational DBMSs, most Semantic Web [29, 30].
DBMSs and Reasoners are typically running on server Cloud Databases are designed to be run in the platforms, and all other types of DBMSs usually ofer cloud, where (storage and computing) resources can a local mode to run on a single server. be dynamically allocated and freed according to users’
Hardware-Accelerated Servers speed up database demands. Hence, cloud databases must consider that tasks by utilizing the massive parallelism of special nodes (for storing and computing) are joining and leavhardware behind today’s multi-core CPUs. ing, such that it may be necessary to redistribute data
Modern Graphical Processing Units (GPUs) consist and to react for processing jobs on leaving nodes. Furof several thousand computing cores, which follow the thermore, as the nodes are typically not high-end hardsingle-instruction multiple-data paradigm, i.e., the same ware like servers with redundant components and instruction is executed on diferent data on diferent clouds consist of many more nodes (up to several thoucores at the same time. GPUs are often regarded as sand nodes), hardware and communication failures may special form of many-core CPUs. Hence, neither all occur more often. Hence, cloud computing architecparallel algorithms are suitable for nor benefit from tures apply simple fault-tolerance mechanisms by reGPUs. However, the massive parallel processing of ex- peating crashed jobs. Table 2 contains an overview 11Note that single installations of DBMSs can only be accessed via their ofered APIs or by setting up subqueries (of the global query) to them, which hinders the full potential of optimized processing of e.g. joins between the data of the diferent DBMSs.
12investigated by Volkswagen,
https://www.volkswagenag.com/en/news/stories/
2018/11/intelligent-trafic-control-with-quantum-computers.html
(accessed on 17.12.2020)
see
over important state-of-the-art Big Data analytics en- a new form of cloud: the web cloud [35]: One just
gines working in cloud environments. Additionally visits with his/her web browser a certain webpage in
to one-time queries, Apache Spark and Apache Flink order to connect his/her computer to the web cloud.
ofer to process data streams and continuous queries, In this way the setup of the web cloud is much easier
such that they also belong to the type of than those of traditional clouds. Furthermore, the web
stream databases. There exists various examples of cloud has a much larger number of potential nodes, as
Semantic Web databases on top of the diferent Cloud any computer running a browser may connect to and
technologies like [
Mobile Databases [38] involve the technical infras- the cloud at uptimes. tructure of mobile providers like base stations (being Besides many approaches to semantic IoT like cornear-by to their connected mobile devices) in order to responding ontologies [47] and interoperability issues speed up processing, lower communication (and hence [48], there are not so many contributions to semanalso energy) costs, increase availability and durability tic IoT databases. IoT databases are often organized (by logging at the base stations instead on mobile de- as P2P database, especially if they work on the fog or vices) in order to overcome limitations of the mobile edge, or follow the dew computing concept. Hence devices. Some RDF stores like [39] are especially de- contributions to P2P networks processing Semantic Web signed to run on mobile devices, but they do not con- data like [41] are relevant for semantic IoT databases sider the backend of mobile providers so far. as well. One of the big challenges here is the distribu
P2P Databases [40, 41] use peer-to-peer (P2P) net- tion of data and processing tasks between cloud and works as underlying backend technology to master a IoT infrastructure including the devices themselves. frequent joining and leaving of nodes for data stor- Furthermore, IoT devices often generate data streams, ing and processing. In comparison to clouds, they are such that organizing the IoT database as stream database designed for a much more frequent change in their is a reasonable choice: The IoT application design may topology and for an equal distribution of functional- especially consider to reduce data by aggregation and ity without distinction of master and slave nodes. P2P focusing on only relevant data, which should be done databases have to introduce more redundancy in data nearby the things. One research direction may constoring as well as even in processing in order to over- sider how to use Semantic Web technologies for defincome the frequent disconnections to their nodes. Fur- ing such aggregation tasks. Reasoning at data sources thermore, P2P databases must consider heterogeneity or nearby, or in clouds is another dificult question and in the connected nodes much more than other types of not so easy to answer in comparison to query processdatabases. There exist already quite many approaches ing on the fog or edge, as reasoning consumes much for semantic data processing in P2P networks like [41], more processing resources. but ontology inference is considered only on a rudimentary basis and for trigger and continuous queries 3.2. (S)HM3P Databases and their not at all [16]. Challenges
IoT Databases [42] are especially developed to serve as data store for large-scale installations of the Internet- HM3P databases are single installations of a M3P DBMS, of-Things (IoT). IoT databases often operate in the cloud, which are not only able to run on multiple platforms, but the communication bootleneck from the IoT de- but runs and tightly integrates diferent types of DBMSs vices to the cloud doesn’t scale especially for IoT de- for ease of use and optimization purposes at runtime. vices with high velocity and large-scale installations. SHM3P databases integrate the diferent types of DBMSs
In companion with the cloud, fog computing [43] in an additional semantic layer and supports global stores and processes data and application logic on near- reasoning over all integrated DBMSs. things edge devices with higher capabilities (rather than IoT databases operating at the same time in clouds primarily in cloud data centers), which saves commu- and on fog, edge or dew computing are reasonable exnication avoiding the route over the internet backbone. amples for H3MP DBMSs: They span over diferent However, fog computing is not really scalable in the platforms, the edge of the IoT network and the cloud number of connected things, as the near-things edge data centers, and have to distribute functionality like devices do not increase in number and capabilities in data aggregation at or near to the things and complex the same way. operations, e.g., natural language processing and rea
The scalability issue is solved in a better way by soning, at the cloud data centers. Furthermore, IoT edge computing [44], which utilizes additionally all databases have to consider diferent types of query proIoT devices for data storage and processing, and ex- cessing by dealing with traditional (one-time) queries ecuting application logic: As more IoT devices are de- on static data, continuous queries on data streams and ployed, as more data needs to be stored and processed, spatial-temporal queries on archived data of data streams. IoT devices are often heterogeneous because they are • developing multi-platform transaction synchronizae.g. developed by diferent manufacturers: the use of tion approaches and supporting global transaction ontologies and hence of semantic databases simplifies synchronization approaches over distributed diferthe integration of these devices. Semantic IoT databases ent transaction synchronization approaches running sometimes manage data at the IoT devices in the tradi- on diferent platforms tional way for performance reasons and only support • combining diferent types of databases (on diferent reasoning and semantic querying at the cloud centers platforms) to ofer the best of these databases and after transforming the data of IoT devices to semantic platforms under one hood to applications and users data [16]. Other approaches support even reasoning transparently or via intelligent integration into query on streams [16]. language and API, e.g., guaranteeing atomicity and
Multi-platform DBMSs are already highly ambitious isolation in transactions for the data stored on a pareven for large, established database companies since it allel server, but not for those data in the cloud suprequires data management skills in an extremely wide porting fast updates spectrum (i.e., data management issues in sensors and smart objects for IoT databases are completely difer- Specific challenges of SHM3P DBMSs are ent from the challenges of in-memory databases of P2P • integrating diferent data models in a semantic layer data oriented systems and semantic querying and rea- on top of the underlying data models soning of Semantic Web databases). Hence current ap- • eficient transformations from and to the semantic proaches are more on interoperability between the va- model in an operational system riety of DBMSs, each one focusing on its specific issues • developing eficient semantic querying and reasonrelated to its specific functionalities. However, we pro- ing over the integrated data of diferent models pose to support a global approach to integrate all these • global reasoning over reasoners running on diferspecific functionalities in order to use their diferent ent platforms supporting some kind of distributed benefits in an uniform way and to increase the overall heterogeneous reasoning benefits of the global approach. • developing a combination of stream reasoning over
New challenges of M3P and HM3P DBMSs in streaming data (e.g. of IoT devices) with static reacomparison to traditional DBMSs and MM-DBMSs are soning over large-scale data sets (stored e.g. in clouds) • developing only one code base for the diferent plat- • supporting transactions over semantic data by inteforms, but not introducing performance overhead in grating the reasoner in transaction synchronization comparison to single platform databases13 • identifying common properties of several platforms We are sure that this is not an exhaustive list of new and reusing those approaches (like fault tolerance challenges. Many further challenges will arise during mechanisms) in diferent combinations, which are developing the (S)(H)M3P DBMSs and considering esbest suitable for these considered platforms pecially combinations of diferent platforms and mod• data distribution among diferent platforms (apply- els at runtime.
ing diferent data distribution approaches as well) • eficient binary serialization and communication pro- 4. Summary and Conclusions tocols for integrating the diferent platforms • data distribution strategies considering overall the Multi-model databases provide the infrastructure to handiferent properties of used platforms and models dle the zoo of data models managed in today’s compa(like fast reads in relational databases on parallel nies. Multi-model databases that are able to run on servers and fast updates in cloud databases) a variety of platforms, which are typically deployed • query optimization and other database tasks across and in use in parallel in today’s companies, are called diferent platforms, which apply diferent database multi-model multi-platform database management sysapproaches tems (M3P DBMSs). Hybrid M3P (HM3P) DBMSs span • dealing with and integrating diferent privacy over diferent platforms at run-time. Our focus is on its and security mechanisms supporting diferent pri- semantic counterpart: Semantic HM3P (SHM3P) DBMSs vacy and security levels in the diferent platforms ofer its additional semantic layer for simple integra(with research e.g. on querying heterogeneous en- tion of the DBMS technologies of its operational platcrypted data) forms. Furthermore, we describe and analyze diferent types of DBMSs and platforms concerning their properties, chances and challenges for DBMSs with spe13We are of the opinion that this is possible by applying Kotlin features like expected and actual declarations for classes and types, and inline functions and classes.