HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce

Abstract

HDT a is binary RDF serialization aiming at minimizing the space overheads of traditional RDF formats, while providing retrieval features in compressed space. Several HDT-based applications, such as the recent Linked Data Fragments proposal, leverage these features for diverse publication, interchange and consumption purposes. However, scalability issues emerge in HDT construction because the whole RDF dataset must be processed in a memory-consuming task. This is hindering the evolution of novel applications and techniques at Web scale. This paper introduces HDT-MR, a MapReduce-based technique to process huge RDF and build the HDT serialization. HDT-MR performs in linear time with the dataset size and has proven able to serialize datasets up to several billion triples, preserving HDT compression and retrieval features.

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1 Introduction

The Resource Description Framework (RDF) was originally proposed as a data model for describing resources in the Web [12], and has evolved into a standard for data interchange in the emergent Web of (Linked) Data. RDF has been widely used in the last years, specially under the Linked Open Data initiative, where it shows its potential for integrating non-structured and semi-structured data from several sources and many varied fields of knowledge. This flexibility is obtained by structuring information as triples: (i) the subject is the resource being described; (ii) the predicate gives a property about the resource; and (iii) the object sets the value of the description. A set of RDF triples is a labeled directed graph, with subjects and objects as nodes, and predicates as edges.

This “graph view” is a mental model that helps to understand how information is organized in RDF, but triples must be effectively serialized in some way for storage and/or interchange. The World Wide Web Consortium (W3C) Working Group addresses this need in the last RDF Primer proposal¹. The considered RDF serialization formats (JSON-LD, RDF/XML or Turtle-based ones) provide different ways of writing down RDF triples, yet all of them serialize an RDF graph as plain text. This is a double-edged sword. On the one hand, serialization is an easy task with no much processing overhead. On the other hand, the resulting serialized files tend to be voluminous because of the verbosity underlying to these formats. Although any kind of universal compressor (e.g. gzip) reduces space requirements for RDF storage and interchange purposes [6], space overheads remain a problem when triples are decompressed for consumption (parsing, searching, etc.). This situation is even more worrying because end-users have, in general, less computational resources than publishers.

HDT (Header-Dictionary-Triples) is an effective alternative for RDF serialization. It is a binary format which reorganizes RDF triples in two main components. The Dictionary organizes all terms used in triples and maps them to numerical identifiers. This decision allows the original graph to be transformed into a graph of IDs encoded by the Triples component. Built-in indexes, in both components, allow RDF triples to be randomly retrieved in compressed space. In other words, HDT outputs more compact files than the aforementioned formats and also enables RDF triples to be efficiently accessed without prior decompression [13]. This fact makes HDT an ideal choice to play as storage engine within semantic applications. HDT-FoQ [13] illustrates how HDT can be used for efficient triple pattern and SPARQL join resolution, while WaterFowl [4] goes a step further and provides inference on top of HDT foundations. This notion of HDT-based store is deployed in applications such as Linked Data Fragments [18], the SemStim recommendation system [8] or the Android app HDTourist [9].

Nevertheless, these achievements are at the price of moving scalability issues to the publishers, or data providers in general. Serializing RDF into HDT is not as simple as with plain formats, given that the whole dataset must be exhaustively processed to obtain the Dictionary and Triples components. Current HDT implementations demand not negligible amounts of memory, so the HDT serialization lacks of scalability for huge datasets (e.g. those having hundreds of millions or billions of triples). Although these datasets are currently uncommon, semantic publication efforts on emerging data-intensive areas (such as biology or astronomy) or integrating several sources into heterogeneous mashups (as RDF excels at linking data from diverse datasets) are starting to face this challenge.

This paper improves the HDT workflow by introducing MapReduce [5] as the computation model for large HDT serialization. MapReduce is a framework for the distributed processing of large amounts of data, and it can be considered as de facto standard for Big Data processing. Our MapReduce-based approach, HDT-MR, reduces scalability issues arising to HDT generation, enabling larger datasets to be serialized for end-user consumption. We perform evaluations scaling up to 5.32 billion triples ($10$ times larger than the largest dataset serialized by the original HDT), reporting linear processing times to the dataset size. This states that HDT-MR provides serialization for RDF datasets of arbitrary size while preserving both the HDT compression and retrieval features [6, 13].

The rest of the paper is organized as follows. Section 2 summarizes the background required to understand our approach, which is fully described in Sect. 3. Section 4 reports experimental results about HDT-MR. Finally, Sect. 5 concludes about HDT-MR and devises some future work around it.

2 Background

This section provides background to understand our current approach. We give basic notions about MapReduce and explain HDT foundations. Then, we compare HDT to the current state of the art of RDF compression.

2.1 MapReduce

MapReduce [5] is a framework and programming model to process large amounts of data in a distributed way. Its main purpose is to provide efficient parallelization while abstracting the complexity of distributed processing. MapReduce is not schema-dependent; unstructured and semi-structured can be processed, at the price of parsing every item [11]. A MapReduce job comprises two phases. The first phase, map, reads the data as pairs key-value $(k1,v1)$ and outputs another series of pairs key-value of different domain $(k2,v2)$. The second phase, reduce, processes the list of values $v2$, related to each key $k2$, and produces a final list of output values $v2$ pertaining to the same domain. Many tasks are launched on each phase, all of them processing a small piece of the input data. The following scheme illustrates input and output data to be processed in each phase:

MapReduce relies on a master/slave architecture. The master initializes the process, distributes the workload among the cluster and manages all bookkeeping information. The slaves (or workers) run map and reduce tasks. The workers commonly store the data using a distributed filesystem based on the GFS (Google File System) model, where data are split in small pieces and stored in different nodes. This allows workers to leverage data locality as much as possible, reading data from the same machine where the task runs [5]. MapReduce performs exhaustive I/O operations. The input of every task is read from disk, and the output is also written on disk. It is also intensive in bandwidth usage. The map output must be transferred to reduce nodes and, even if most of the map tasks read their data locally, part of them must be gathered from other nodes.

Apache Hadoop² is currently the most used implementation of MapReduce. It is designed to work in heterogeneous clusters of commodity hardware. Hadoop implements HDFS (Hadoop Distributed File System), as distributed filesystem providing data replication. It replicates each split of data in a number of nodes (commonly three), improving data locality and also providing fault tolerance.

2.2 HDT

HDT³ [6] is a binary serialization format optimized for RDF storage and transmission. Besides, HDT files can be mapped to a configuration of succinct data structures which allows the inner triples to be searched and browsed efficiently.

Fig. 1.

HDT Dictionary and Triples configuration for an RDF graph.

HDT encodes RDF into three components carefully described to address RDF peculiarities within a Publication-Interchange-Consumption workflow. The Header (H) holds the dataset metadata, including relevant information for discovering and parsing, hence serving as an entry point for consumption. The Dictionary (D) is a catalogue that encodes all the different terms used in the dataset and maps each of them to a unique identifier: ID. The Triples (T) component encodes the RDF graph as a graph of IDs, i.e. representing tuples of three IDs. Thus, Dictionary and Triples address the main goal of RDF compactness. Figure 1 shows how the Dictionary and Triples components are configured for a simple RDF graph. Each component is detailed below.

Dictionary. This component organizes the different terms in the graph according to their role in the dataset. Thus, four sections are considered: the section SO manages those terms playing both as subject and object, and maps them to the range [1, |SO|], being |SO| the number of different terms acting as subject and object. Sections S and O comprise terms that exclusively play subject and object roles respectively. Both sections are mapped from |SO|+1, ranging up to |SO|+|S| and |SO|+|O| respectively, where |S| and |O| are the number of exclusive subjects and objects. Finally, section P organizes all predicate terms, which are mapped to the range [1, |P|]. It is worth noting that no ambiguity is possible once we know the role played by the corresponding ID.

Each section of the Dictionary is independently encoded to grasp its particular features. This allows important space savings to be achieved by considering that this sort of string dictionaries are highly compressible [14]. Nonetheless, efficient encoding of string dictionaries [2] is orthogonal to the current problem, hence it is not addressed in this paper.

Triples. This component encodes the structure of the RDF graph after ID substitution. That is, RDF triples are encoded as groups of three IDs (ID-triples hereinafter): $\mathtt{(id}_s \; \mathtt{id}_p \; \mathtt{id}_o)$, where $\mathtt{id}_s$, $\mathtt{id}_p$, and $\mathtt{id}_o$ are respectively the IDs of the corresponding subject, predicate, and object terms in the Dictionary. The Triples component organizes all triples into a forest of trees, one per different subject: the subject is the root; the middle level comprises the ordered list of predicates reachable from the corresponding subject; and the leaves list the object IDs related to each (subject, predicate) pair. This underlying representation (illustrated in Fig. 1) is effectively encoded following the BitmapTriples approach [6]. In brief, it comprises two sequences: Sp and So, concatenating respectively all predicate IDs in the middle level and all object IDs in the leaves; and two bitsequences: Bp and Bo, which are respectively aligned with Sp and So, using a 1-bit to mark the end of each list.

Building HDT. Once Dictionary and Triples internals have been described, we proceed to summarize how HDT is currently built⁴. Remind that this process is the main scalability bottleneck addressed by our current proposal.

To date, HDT serialization can be seen as a three-stage process:

• Classifying RDF Terms. This first stage performs a triple-by-triple parsing (from the input dataset file) to classify each RDF term into the corresponding Dictionary section.  To do so, it keeps a temporal data structure, consisting of three hash tables storing subject-to-ID, predicate-to-ID, and object-to-ID mappings. For each parsed triple, its subject, predicate, and object are searched in the appropriate hash, obtaining the associated ID if present. Terms not found are inserted and assigned an auto-incremental ID. These IDs are used to obtain the temporal ID-triples $\mathtt{(id}_s \; \mathtt{id}_p \; \mathtt{id}_o$ ) representation of each parsed triple, storing all them in a temporary ID-triples array. At the end of the file parsing, subject and object hashes are processed to identify terms playing both roles. These are deleted from their original hash tables and inserted into a fourth hash comprising terms in the SO section.

• Building HDT Dictionary. Each dictionary section is now sorted lexicographically, because prefix-based encoding is a well-suited choice for compressing string dictionaries [2]. Finally, an auxiliary array coordinates the previous temporal ID and the definitive ID after the Dictionary sorting.

• Building HDT Triples. This final stage scans the temporary array storing ID-triples. For each triple, its three IDs are replaced by their definitive IDs in the newly created Dictionary. Once updated, ID-triples are sorted by subject, predicate and object IDs to obtain the BitmapTriples streams. In practice, it is a straightforward task which scans the array to sequentially extract the predicates and objects into the Sp and So sequences, and denoting list endings with 1-bits in the bitsequences.

2.3 Related Work

HDT was designed as a binary serialization format, but the optimized encodings achieved by Dictionary and Triples components make HDT also excels as RDF compressor. Attending to the taxonomy from [16], HDT is a syntactic compressor because it detects redundancy at serialization level. That is, the Dictionary reduces symbolic redundancy from the terms used in the dataset, while the Triples component leverages structural redundancy from the graph topology.

To the best of our knowledge, the best space savings are reported by syntactic compressors. Among them, k$^2$-triples [1] is the most effective approach. It performs a predicate-based partition of the dataset into subsets of pairs (subject, object), which are then encoded as sparse binary matrices (providing direct access to the compressed triples). k$^2$-triples achievements, though, are at the cost of exhaustive time-demanding compression processes that also need large amounts of main memory. On the other hand, logical compressors perform discarding triples that can be inferred from others. Thus, they achieve compression because only encode a “primitive” subset of the original dataset. Joshi et al. [10] propose a technique which prunes more than 50 % of the triples, but it does not achieve competitive numbers regarding HDT, and its compression process also reports longer times. More recently, Pan, et al. [16] propose an hybrid compressor leveraging syntactic and semantic redundancy. Its space numbers slightly improves the less-compressed HDT configurations, but it is far from k$^2$-triples. It also shows non-negligible compression times for all reported datasets.

Thus, the most prominent RDF compressors experience lack of scalability when compressing large RDF datasets. This issue has already been addressed by using distributed computation. Urbani et al. [17] propose an algorithm based on dictionary encoding. They perform a MapReduce job to create the dictionary, where an ID is assigned to each term. The output of this job are key-value pairs, where the key is the ID and the value contains the triple identifier to which the term belongs, and its role on it. Then, another MapReduce job groups by triple and substitutes the terms by their ID. This work makes special emphasis on how RDF skewness can affect MapReduce performance, due to the fact that many terms can be grouped and sent to the same reducer. To avoid this problem, a first job is added, where the input data are sampled and the more popular terms are given their ID before the process starts. Finally, Cheng et al. [3] also perform distributed RDF compression on dictionary encoding. They use the parallel language X10, and report competitive results.

3 HDT-MR

This section describes HDT-MR, our MapReduce-based approach to serialize large RDF datasets in HDT. Figure 2 illustrates the HDT-MR workflow, consisting in two stages: (1) Dictionary Encoding (top) and (2) Triples Encoding (bottom), described in the following subsections. The whole process assumes the original RDF dataset is encoded in N-Triples format (one statement per line).

3.1 Process 1: Dictionary Encoding

This first process builds the HDT Dictionary from the original N-Triples dataset. It can be seen as a three-task process of (i) identifying the role of each term in the dataset, (ii) obtaining the aforementioned sections (SO, S, O, and P) in lexicographic order, and (iii) effectively encoding the Dictionary component.

Fig. 2.

HDT-MR workflow.

We design HDT-MR to perform these three tasks as two distributed MapReduce jobs and a subsequent local process (performed by the master node), as shown in Fig. 2. The first job performs the role identification, while the second is needed to perform a global sort. Finally, the master effectively encodes the Dictionary component. All these sub-processes are further described below.

Job 1.1: Roles Detection. This job parses the input N-Triples file to detect all roles played by RDF terms in the dataset. First, mappers perform a triple-by-triple parsing and output (key,value) pairs of the form (RDF term, role), in which role is $S$ (subject), $P$ (predicate) or $O$ (object), according to the term position in the triple. It is illustrated in Fig. 3, with two processing nodes performing on the RDF used in Fig. 1. For instance, (ex:P1, $S$ ), (ex:worksFor, $P$ ), and (ex:D1, $O$ ) are the pairs obtained for the triple (ex:P1, ex:worksFor, ex:D1).

These pairs are partitioned and sorted among the reducers, which group the different roles played by a term. Note that RDF terms including roles $S$ and $O$, result in pairs (RDF term, $SO$ ). Thus, this job outputs a number of lexicographically ordered lists (RDF term, roles); there will be as many lists as reducers on the cluster. Algorithm 1 shows the pseudo-code of these jobs.

Finally, it is important to mention that a combiner function is used at the output of each map. This function is executed on each node before the map transmits its output to the reducers. In our case, if a mapper emits more than one pair (RDF term, role) for a term, all those pairs are grouped into a single one comprising a list of all roles. It allows the bandwidth usage to be decreased by grouping pairs with the same key before transferring them to the reducer.

Fig. 3.

Example of dictionary encoding: roles detection (Job 1.1).

Job 1.2: RDF Terms Sectioning. The previous job outputs several lists of pairs (RDF term, roles), one per reduce of previous phase, each of them sorted lexicographically. However, the construction of each HDT Dictionary section requires a unique sorted list. Note that a simple concatenation of the output lists would not fulfill this requirement, because the resulting list would not maintain a global order. The reason behind this behavior is that, although the input of each reducer is sorted before processing, the particular input transmitted to each reducer is autonomously decided by the framework in a process called partitioning. By default, Hadoop hashes the key and assigns it to a given reducer, promoting to obtain partitions of similar sizes. Thus, this distribution does not respect a global order of the input. While this behavior may be changed to assign the reducers a globally sorted input, this is not straightforward.

A naïve approach would be to use a single reducer, but this would result extremely inefficient: the whole data had to be processed by a single machine, losing most of the benefits of distributed computing that MapReduce provides. Another approach is to manually create partition groups. For instance, we could send terms beginning with the letters from a to c to the first reducer, terms beginning with the letters from d to f to the second reducer, and so on. However, partitions must be chosen with care, or they could be the root of performance issues: if partitions are of very different size, the job time will be dominated by the slowest reducer (that is, the reducer that receives the largest input). This fact is specially significant for RDF processing because of its skewed features.

HDT-MR relies on the simple but efficient solution of sampling input data to obtain partitions of similar size. To do so, we make use of the TotalOrderPartitioner of Hadoop. It is important to note that this partitioning cannot be performed while processing a job, but needs to be completed prior of a job execution. Note also that the input domain of the reducers needs to be different from the input domain of the job to identify and group the RDF terms (that is, the job receives triples, while the reducers receive individual terms and roles).

Fig. 4.

Example of dictionary encoding: RDF terms sectioning (Job 1.2).

All these reasons conforms the main motivation to include this second MapReduce job to globally sort the output of the first job. This job takes as input the lists of (RDF term, roles) obtained in the precedent job, and uses role values to sort each term in its corresponding list. In this case, identity mappers deliver directly their input (with no processing) to the reducers, which send RDF terms to different outputs depending on their role. Figure 4 illustrates this job. As only the term is needed, a pair (RDF term, null ) is emitted for each RDF term (nulls are omitted on the outputs). We obtain as many role-based lists as reducers in the cluster, but these are finally concatenated to obtain four sorted files, one per Dictionary section.The pseudo-code for this job is described in Algorithm 2.

Local Sub-process 1.3: HDT Dictionary Encoding. This final stage performs locally in the master node, encoding dictionaries for the four sections obtained from the MapReduce jobs. It means that each section is read line-per-line, and each term is differentially encoded to obtain a Front-Coding dictionary [2], providing term-ID mappings. It is a simple process with no scalability issues.

3.2 Process 2: Triples Encoding

This second process parses the original N-Triples dataset to obtain, in this case, the HDT Triples component. The main tasks for such Triples encoding are (i) replacing RDF terms by their ID in the Dictionary, and (ii) getting the ID-triples encoding sorted by subject, predicate and object IDs. As in the previous process, HDT-MR accomplishes these tasks by two MapReduce jobs and a final local process (see the global overview in Fig. 2), further described below.

Job 2.1: ID-Triples Serialization. This first job replaces each term by its ID. To do so, HDT-MR first transmits and loads the–already compressed and functional–Dictionary (encoded in the previous stage) in all nodes of the cluster. Then, mappers parse N-Triples and replace each term by its ID in the Dictionary. Identity reducers simply sort incoming data and output a list of pairs (ID-triple, null ). We can see this process in action in Fig. 5, where the terms of each triple are replaced by the IDs given in the previous example (note that nulls are omitted on the outputs). The output of this job is a set of lexicographically ordered lists of ID-Triples; there will be as many lists as reducers on the cluster. The pseudo-code of this job is illustrated in Algorithm 3.

Fig. 5.

Example of triples encoding: ID-triples serialization (Job 2.1).

Job 2.2: ID-Triples Sorting. Similarly to the first process, Triples Encoding requires of a second job to sort the outputs. Based on the same premises, HDT-MR makes use of Hadoop TotalOrderPartitioner to sample the output data from the first job, creating partitions of a similar size as input for the second job. Then, this job reads the ID-triples representation generated and sorts it by subject, predicate and object ID. This is a very simple job that uses identity mappers and reducers. As in the previous job, ID-triples are contained in the key and the value is set to null. In fact, all the logic is performed by the framework in the partitioning phase between map and reduce, generating similar size partitions of globally sorted data. Figure 6 continues with the running example and shows the actions performed by this job after receiving the output of the previous job (note again that nulls are omitted on the outputs).

Fig. 6.

Example of triples encoding: ID-triples sorting (Job 2.2)

Local Sub-process 2.3: HDT Triples Encoding. This final stage encodes the ID-triples list (generated by the previous job) as HDT BitmapTriples [6]. It is performed locally in the master node as in the original HDT construction. That is, it sequentially reads the sorted ID-triples to build the sequences Sp and So, and the aligned bitsequences Bp and Bo, with no scalability issues.

4 Experimental Evaluation

This section evaluates the performance of HDT-MR, the proposed MapReduce-based HDT construction, and compares it to the traditional single-node approach. We have developed a proof-of-concept HDT-MR prototype (under the Hadoop framework: version 1.2.1) which uses the existing HDT-Java library⁵ (RC-2). This library is also used for the baseline HDT running on a single node.

The experimental setup is designed as follows (see Table 1). On the one hand, we use a powerful computational configuration to implement the role of data provider running HDT on a single node. On the other hand, we deploy HDT-MR using a potent master and 10 slave nodes running on a more memory-limited configuration. This infrastructure tries to simulate a computational cluster in which further nodes may be plugged to process huge RDF datasets. For a fair comparison, the amount of main memory in the single node is the same as the total memory available for the full cluster of Hadoop.

Table 1. Experimental setup configuration.

Table 2. Statistical dataset description.

Regarding datasets, we consider a varied configuration comprising real-world and synthetic ones. All of them are statistically described in Table 2. Among the real-world ones, we choose them based on their volume and variety, but also attending to their previous uses for benchmarking. Ike ⁶ comprises weather measurements from the Ike hurricane; LinkedGeoData ⁷ is a large geo-spatial dataset derived from Open Street Map; and DBPedia 3.8⁸ is the well-known knowledge base extracted from Wikipedia. We also join these real-world datasets in a mashup which comprises all data from the three data sources. On the other hand, we use the LUBM [7] data generator to obtain synthetic datasets. We build “small datasets” from 1,000 ($0.13$ billion triples) to 8,000 universities ($1.07$ billion triples). From the latter, we build datasets of incremental size (4,000 universities: $0.55$ billion triples) up to 40,000 universities ($5.32$ billion triples).

Table 2 also shows original dataset sizes both in plain NTriples (NT) and compressed with lzo. It is worth noting that HDT-MR uses lzo to compress the datasets before storing them in HDFS. This format allows for compressed data to be split among the reducers, and provides storage and reading speed improvements [15]. As can be seen, our largest dataset uses $730.9$ GB in NTriples, and this spaces is reduced up to $52.9$ GB with lzo compression.

Fig. 7.

Serialization times: HDT-Java vs HDT-MR.

Fig. 8.

Serialization times: HDT-MR.

Figure 7 compares serialization times for HDT-Java and HDT-MR, while Fig. 8 shows HDT-MR serialization times for those datasets where HDT-Java is unable to obtain the serialization. These times are averaged over three independent serialization processes for each dataset. As can be seen, HDT-Java reports an excellent performance on real-world datasets, while our current approach only achieves a comparable time for Ike. This is an expected result because HDT-Java runs the whole process in main-memory while HDT-MR relies on I/O operations. However, HDT-Java crashes for the mashup because the 128 GB of available RAM are insufficient to process such scale in the single node. The situation is similar for the LUBM datasets: HDT-Java is the best choice for the smallest datasets, but the difference decreases with the dataset size and HDT-MR shows better results from LUBM-5000 (0.67 billion triples). HDT-Java fails to process datasets from LUBM-8000 (1.07 billion triples) because of memory requirements. This is the target scenario for HDT-MR, which scales to the LUBM-40000 without issues. As can be seen in both figures, serialization times increase linearly with the dataset size, and triples encoding remains the most expensive stage.

RDF compression is not the main purpose of this paper, but it is worth emphasizing HDT space numbers, as previous literature does not report compression results for such large datasets. These numbers are also summarized in Table 2. HDT always reports smaller sizes than the original datasets compressed with lzo. For instance, HDT serializes LUBM-40000 using 19.7 GB less than NT+lzo. The difference increases when compressed with gzip. For LUBM-40000, HDT+gz uses 42.5 GB less than NT+lzo. In practice, it means that HDT+gz uses 5 times less space than NT+lzo. Finally, it is worth remembering that HDT-MR obtains the same HDT serialization than a mono-node solution, hence achieving the same compression ratio and enabling the same query functionality. Source code and further details on HDT-MR are available at the HDT-MR project⁹.

5 Conclusions and Future Work

HDT is gaining increasing attention, positioning itself as the de facto baseline for RDF compression. Latest practical applications exploit the HDT built-in indexes for RDF retrieval with no prior decompression, making HDT evolve to a self-contained RDF store. In this paper we introduce HDT-MR, a technique tackling scalability issues arising to HDT construction at very large scale. HDT-MR lightens the previous heavy memory-consumption burden by moving the construction task to the MapReduce paradigm. We present the HDT-MR distributed workflow, evaluating its performance against the mono-node solution in huge real-world and benchmarking RDF datasets, scaling up to more than 5 billion triples. Results show that HDT-MR is able to scale up to an arbitrary size in commodity clusters, while the mono-node solution fails to process datasets larger than 1 billion triples. Thus, HDT-MR greatly reduces hardware requirements for processing Big Semantic Data.

Our future work focuses on two directions. First, we plan to exploit HDT-MR achievements as these can be directly reused by the HDT community, fostering the development of novel applications working at very large scale. Finally, our research consider to combine HDT and MapReduce foundations to work together on other Big Semantic Data tasks, such as querying and reasoning.