Han Meng

Marie-Annick A Galland
email: marie-annick.galland@ec-lyon.fr

Mohamed Ichchou
email: mohamed.ichchou@ec-lyon.fr

F X Xin

T J Lu

On the low frequency acoustic properties of novel multifunctional honeycomb sandwich panels with micro-perforated faceplates

Keywords: sandwich structures, honeycomb, microperforated, multifunctional, sound absorption, sound transmission loss

Introduction

Sandwich panels are lightweight structures composed of thin faceplates with inserted low density cores. With high stiffness-to-density ratio, good thermal and acoustic properties, sandwich panels have been widely applied in packaging, transportation, aerospace, and building/construction fields. Nonetheless, optimization issues and better understandings of the multi-physical behaviors of sandwich structures remain challenging questions for both academics and industrials.

The concern in what follows is about improving the vibroacoustic properties of sandwich panels. The STL of a sandwich panel, for instance, has attracted numerous investigations. These investigations can be classified by the configuration of core inside the sandwich panel. The simplest sandwich panel is made of double walls with internal air layer. London [START_REF] London | Transmission of reverberant sound through double walls[END_REF], Antonio et al. [START_REF] António | Analytical evaluation of the acoustic insulation provided by double infinite walls[END_REF], Chazot and Guyader [START_REF] Chazot | Prediction of transmission loss of double panels with a patch-mobility method[END_REF], and Wang et al. [START_REF] Wang | Predicting the Sound Transmission Loss of Sandwich Panels by Statistical Energy Analysis Approach[END_REF] calculated the STL of both infinite and finite sized double walls separated by air gaps using analytical modeling and statistical energy analysis. To improve the sound insulation capacity, porous elastic materials are added to fill the gap between the two walls (faceplates), and the lined porous material can be either bounded or unbouned to the faceplates. Bolton et al. [START_REF] Bolton | Normal incidence sound transmission through double-panel systems lined with relatively stiff, partially reticulated polyurethane foam[END_REF][START_REF] Bolton | Sound transmission through multi-panel structures lined with elastic porous materials[END_REF], Panneton and Atalla [START_REF] Panneton | Numerical prediction of sound transmission through finite multilayer systems with poroelastic materials[END_REF], Kang and Bolton [START_REF] Kang | A finite element model for sound transmission through foamlined double-panel structures[END_REF] presented theoretical and numerical investigations for the transmission loss of double panels lined with porous materials, with the latter described using the Biot theory [START_REF] Biot | Theory of propagation of elastic waves in a fluid-saturated porous solid. I. Low-frequency range[END_REF][START_REF] Biot | Theory of propagation of elastic waves in a fluid-saturated porous solid. II. Higher frequency range[END_REF]. They found that the highest STL can be obtained if the porous material is bounded to one faceplate and separated from the other one. Except from porous elastic materials, Chazot and Guyader [START_REF] Chazot | Transmission loss of double panels filled with porogranular materials[END_REF], Doutres and Atalla [START_REF] Doutres | Acoustic contributions of a sound absorbing blanket placed in a double panel structure: Absorption versus transmission[END_REF], and Ghanbari et al. [START_REF] Ghanbari | Studying thin film damping in a microbeam resonator based on non-classical theories[END_REF] also conducted analytical investigations for acoustic properties of double panels with porogranular materials, thin film damping and multilayer porous blankets, while Zielinski et al. [START_REF] Zielinski | Fully coupled finite-element modeling of active sandwich panels with poroelastic core[END_REF] and Hu et al. [START_REF] Hu | Acoustic transmission performance of double-wall active sound packages in a tube: numerical/experimental validations[END_REF], Melon et al. [START_REF] Melon | One dimensional study of a module for active/passive control of both absorption and transmission[END_REF] numerically investigated the sound insulation and transmission properties of active hybrid sandwich panels with porous absorbent material layers in either active or passive mode. Compared with double panels separated by air, sandwich panels filled with porous sound absorbing materials have better sound insulation. However, due to their intrinsically low stiffness, the lowfrequency transmission loss of sandwich panels filled with porous materials is yet sufficient. Therefore, many investigators resorted to sandwich panels cored with solid connecting structures to achieve higher stiffness and better low frequency transmission loss, including sandwich panels with stiffened faceplates, corrugated and honeycomb cores. Shen et al. [START_REF] Shen | Theoretical model for sound transmission through finite sandwich structures with corrugated core[END_REF] conducted analytical studies for simply supported finite sandwich panels with corrugated cores by employing an equivalent structure of double panels connected by rotational and translational springs. Sun and Liu [START_REF] Sun | Vibration analysis of hard-coated composite beam considering the strain dependent characteristic of coating material[END_REF] analyzed the vibration of composite panels with hard coating. Mead [START_REF] Mead | Wave propagation in continuous periodic structures: research contributions from Southampton 1964-1995[END_REF], Wang et al. [START_REF] Wang | Sound transmission through lightweight double-leaf partitions: Theoretical modelling[END_REF], Craik and Smith [START_REF] Craik | Sound transmission through lightweight parallel plates. Part II: structure-borne sound[END_REF] proposed various theories to investigate the transmission loss of double panels connected by parallel plates. Xin and Lu [START_REF] Xin | Sound radiation of orthogonally rib-stiffened sandwich structures with cavity absorption[END_REF] and Shen et al. [START_REF] Shen | Sound radiation of orthogonally stiffened laminated composite plates under airborne and structure borne excitations[END_REF] extended the theories for double plates reinforced with parallel rib stiffeners to orthogonal rib stiffeners. In comparison, sandwich panels with honeycomb cores are the most popular sandwich constructions in application. In addition to characterizing their structural behavior, many studies have also been carried out to characterize their vibroacoustic behavior. For typical instance, Kumar et al. [START_REF] Kumar | Predicting the sound transmission loss of honeycomb panels using the wave propagation approach[END_REF], Jung et al. [START_REF] Jung | A Study on Transmission Loss Characteristics of Honeycomb Panel for Offshore Structures[END_REF], Huang and Ng [START_REF] Huang | Sound insulation improvement using honeycomb sandwich panels[END_REF],

and Ng and Hui [START_REF] Ng | Low frequency sound insulation using stiffness control with honeycomb panels[END_REF] presented theoretical models to predict the STL of honeycomb sandwiches, while Griese et al. [START_REF] Griese | The Effect of Honeycomb Core Geometry on the Sound Transmission Performance of Sandwich Panels[END_REF] and Feng and Kumar [START_REF] Feng | On application of radiation loss factor in the prediction of sound transmission loss of a honeycomb panel[END_REF] employed numerical and experimental methods to investigate their STL.

Apart from the STL, SAC has also become a noticeable issue for investigators of sandwich panels design. In general, even though efficient in STL, sandwich panels are poor sound absorbers. Instead, micro perforated panels (MPPs) can provide good SAC and hence have been widely applied as sound absorbers. A MPP absorber is comprised of a thin plate with perforated submillimeter pores, an air cavity and a rigid wall.

Compared with conventional porous absorbing materials, MPP absorbers can provide sufficient wideband absorption at low frequencies. Based on the method of electroacoustic analogy, Maa [START_REF] Maa | Theory and design of microperforated panel sound-absorbing constructions[END_REF][START_REF] Maa | Potential of microperforated panel absorber[END_REF] developed the most popular theory for MPPs. Atalla and Sgard [START_REF] Atalla | Modeling of perforated plates and screens using rigid frame porous models[END_REF] also presented an analytical model to calculate the SAC of MPPs by making use of the theory for rigid frame porous materials.

MPPs can be made of metal, plastic, plywood, acryl glass and sheet material, thus suitable for many environments including even severe conditions. For example, Asdrubali and Pispola [START_REF] Asdrubali | Properties of transparent sound-absorbing panels for use in noise barriers[END_REF] applied transparent MPPs as noise barriers, Li and Mechefske [START_REF] Li | A comprehensive experimental study of micro-perforated panel acoustic absorbers in MRI scanners[END_REF] experimentally investigated the application of MPPs in magnetic resonance imaging scanners, Sakagami et al. [START_REF] Sakagami | Application of microperforated panel absorbers to room interior surfaces[END_REF] presented investigations of MPP absorbers as room interior surface.

To obtain lightweight structures with good absorption and insulation properties, combinations of MPPs and sandwich panels come into view of several researchers. Dupont et al. [START_REF] Dupont | Acoustic properties of lightweight micro-perforated plate systems[END_REF] investigated an infinite MPP-solid plate coupling structure, both theoretically and experimentally. Tang et al. [START_REF] Tang | Heterogeneously perforated honeycomb-corrugation hybrid sandwich panel as sound absorber[END_REF] created a lightweight rigid-frame sandwich panel with perforated honeycomb-corrugation hybrid core as well as perforated faceplates, which exhibited perfect sound absorption at low frequencies.

Bravo et al. [START_REF] Bravo | Sound absorption and transmission through flexible micro-perforated panels backed by an air layer and a thin plate[END_REF][START_REF] Bravo | Enhancing sound absorption and transmission through flexible multi-layer micro-perforated structures[END_REF] presented a fully coupled modal method to calculate the STL and SAC of finite flexible MPP coupled with plates. Sakagami et al. [START_REF] Sakagami | Effect of a honeycomb on the absorption characteristics of double-leaf microperforated panel (MPP) space sound absorbers[END_REF] analyzed doubleleaf MPPs with inserted honeycomb cores theoretically and found the honeycomb core effective to improve the sound absorption at around resonance peak. Toyoda and Takahashi [START_REF] Toyoda | Sound transmission through a microperforated-panel structure with subdivided air cavities[END_REF] inserted subdivisions to the middle layer of a MPP-plate structure, achieving improved STL at mid frequencies. These studies demonstrated that MPPplate sandwich structures have good STL as well as SAC.

Based on the MPP-plate coupling strategy, a novel multifunctional structure that combines honeycomb sandwich panels with perforated faceplates is developed in the present paper. Honeycomb sandwich panels have great STL (and excellent mechanical properties), and the perforated faceplates can act as sound absorbing structures. Section 2 gives a specific description of the investigated structure. Section 3 presents an analytical model to calculate the STL and SAC, with the influence of core configurations embodied in the analytical model. Finite element (FE) models are subsequently developed for validation in Section 4. Perforated faceplates are shown to improve significantly the STL and SAC of honeycomb sandwich panels at low frequencies by systematic parameter studies presented in Section 5. Results obtained in the present paper can be helpful for the design of multifunctional sandwich panels having excellent mechanical properties and superior acoustic properties at low frequencies.

Problems description

Consider an ultra-lightweight sandwich panel with hexagonal honeycomb core shown schematically in Fig. 1. Its faceplates and core are both made of homogeneous and isotropic material. The top faceplate is a micro-perforated plate with submillimeter pores to enable sound penetration into the air cavities for absorption. It should be noted that in the structure, to achieve high structural efficiency, the thickness of either faceplate is much smaller than that of the middle layer (i.e., the core), while the wall thickness of the core is much smaller than its edge length. Except from the hexagonal honeycomb, other honeycombs, such as rectangular or triangular honeycombs shown in Fig. 2 can also act as the core of the sandwich. The sandwich panels with microperforated faceplates shown in Figs. 1 and2 are attractive for multifunctional applications requiring lightweight and simultaneous load carrying, sound insulation as well as sound absorption capabilities. ignored in existing papers for micro-perforated panels such as Ref [START_REF] Toyoda | Sound transmission through a microperforated-panel structure with subdivided air cavities[END_REF], are first integrated in the analytical model for the calculation of faceplate displacements.

Theory

For a normally incident sound wave, the incident sound pressure

( ) i p z r, and
velocity   , i v z r
are given as:

    0 0 0 0 0 0 , , jk z jk z i i p z Pe v z Pe c       r r (1)
where 0 0 / k =ω c is the wave number, ω is the angular frequency, 0 c is the sound speed, 0 ρ is the density of air and 0 P is the amplitude of the incident wave. For simplification, the time factor -jωt e is suppressed and 0 1 P = is set in the present paper.

In the field inside the honeycomb core between the faceplates, the sound pressure

  2 , p z r can be given as [41]       0 1 0 1 2 , jk z h jk z h p z Ce De      r (2)
where C and D are unknown parameters to be determined, and 1 h is the thickness of the micro-perforated faceplate. Accordingly, the velocity inside the honeycomb can be obtained as:

          0 1 0 1 2 2 0 0 0 , 1 1 , = jk z h jk z h p z v z Ce De j z c          r r (3)
According to the Green's function, sound pressures on the surfaces of the faceplates in the incident and transmitted sound fields are separately given by [START_REF] Takahashi | Sound transmission through single plates with absorptive facings: Improved theory and experiment[END_REF] 

         1 0 1 00 0 1 00 2 ,0 H 2 i p p k v d         r r r r r r (4)         1 0 3 0 0 0 30 0 H 2 p k v d        r r r r r (5)
where ( )

1 0
H is the first kind Hankel function of zero order. The velocity adjacent to the micro-perforated faceplate is given as [START_REF] Toyoda | Reduction of acoustic radiation by impedance control with a perforated absorber system[END_REF][START_REF] Toyoda | Reduction of acoustic radiation by perforated board and honeycomb layer systems[END_REF] 

         1 2 1 , = 1 p f v v h v v      r r r r (6)
where   p v r is the velocity of the perforated faceplate,   f v r is the velocity of the fluid inside the perforated pores, and σ is the perforation ratio of the plate. For a sandwich panel with hexagonal section core, the perforation ratio is calculated by:

2 2 = 6 3 σ πd a ( 7
)
where a is the edge length of the honeycomb, and d is the diameter of the perforated pores as shown in Fig. 1. Likewise, for sandwich panels with rectangular and triangular honeycomb cores, the perforation ratios can be obtained by:

2 = 4 r rr σ πd a b (8) 2 2 3 t t σ πd a = (9)
where r a and r b are the side length of the rectangular honeycomb, and t a is the edge length of the triangular honeycomb as shown in Fig. 2. The pressure difference between the two surfaces of the perforated faceplate is related to the velocity and impedance, given as:

          resist f p react f p r Z v v Z v     r r r (10)
where resist Z and react Z are the resistance and reactance of the perforated faceplate.

According to Maa's theory, the resistance and reactance of a MPP are given as:

2 1 2 1 0 1 2 1 32 2 1 32 32 1 0.85 Z 1 9 /2 resist react h X dX Z d h d i h h X                          (11)
where

0 2 X d ρ ω η = .
Upon combing Eq. ( 6) with Eq. ( 10), the velocity ( )

1
v r can be given as:

          1 1 2 1 + , p v j w p p h      r r r r (12)
where

  0 1 react Z Z     , 0 γ=σ Z and 0 resist react Z =Z Z + .
Next, the fluid velocity adjacent to the lower face plate can be given as:

      3 2 1 b , v v h H j w     r r r (13)
Substitution of Eqs. ( 3) and (2) into Eqs. ( 12) and ( 13) leads to:

            2 1 0 0 p 1 , = = + v h C D c j w p C D        r r r (14)
      0 0 H 2 1 0 0 b , jk jk H v h H Ce De c j w        r r (15)
Combing Eqs. ( 14) and ( 15), one can express the unknown parameters C and D as:

                0 0 0 0 0 0 0 0 1 2 2 0 0 b 0 0 1 1+ e + e 1 + e + e jk H b p jk H jk H jk H p D c c j w j w p Q C c j w c j w p Q                             r r r r r r (16)
where

  0 2 0 0 0 0 1+ e 1+ jk H Q c c       .
For the case that the two faceplates are connected (bonded) by the honeycomb core, the core is considered as rigid, and the displacements of the faceplates are regarded as consistent [START_REF] Toyoda | Sound transmission through a microperforated-panel structure with subdivided air cavities[END_REF], namely:

    p b w w  r r (17)
where the subscripts "p" and "b" denote the perforated faceplate and the bottom faceplate without perforation, respectively. The displacements of the two faceplates are governed by: The total pressure acting on the two faceplates can be given as:

      4 2 , , , p b p b p b D m w p      r r (18) where
                1 2 1 b 2 1 3 , , + p p p p h I p p h H p I         r r r r r r r r (19)
where   I r is the force exerted by the honeycomb core.

According to Eqs. ( 2) and ( 16), it is derived

                2 1 1 2 31 2 1 1 2 31 , = = + , = = b p jkH jkH b p p h C D Q w Q w Q p p h H Ce De G w G w G p        r r r r r r r r (20)
where

    0 0 0 0 0 0 2 2 1 0 0 2 0 0 3 0 0 2 1 00 00 00 2 00 3 00 2e , ( e 1) , (e 1
Q j c QQ j c QQ c Q G c c j c QG j ce QG e c Q                               (21)
Substitution of Eq. ( 20) into Eq. ( 19), yields

                        3 1 1 2 1 2 31 3 1 = + p b p b b p p Q p Qw Qw I p G w G w G p p I           r r r r r r r r r r r (22) Assuming 
      4 2 , , , p b p b p b D m u      r r (23)
According to the definition of the convolution integral, the displacements of the faceplates can be expressed as:

      , , , p b p b p b w p u d        r f r f f (24)
Substituting Eqs. ( 22) and ( 21) into Eq. ( 24), one can write these displacements as:

                                3 1 1 2
1 2 31 3 1 + p b p p b b p b w Q p Qw Qw I u d w Gw Gw Gp p I u d                    r ξ ξ ξ ξ r ξ ξ r ξ ξ ξ ξ ξ r ξ ξ (25)
The force of the honeycomb can be obtained by introducing the Fourier transform to Eq. ( 25) as well as by combing with the convolution theorem,

                              3 1 1 2 b 1 2 3 1 3 2 1 2 + p b p p p b p p b W Q P Q W Q W I U W GW GW GP P I U            k k k k k k k k k k k k k (26)
where

  , x y k k  k and     4 2 , , , =1 2
p b p b p b U D m    k k .   , p b W k ,   1 P k ,
Substitution of Eq. ( 12) into Eq. ( 4) yields:

                  1 0 1 00 0 0 1 0 2 0 1 0 2 ,0 H + , 2 i p p p k j w p p h d             r r r r r r r r (28)
Since the function

    1 0 0 0 H k  r r
is given as:

      0 j 1 0 0 0 2 2 0 1 H = e k d k        k r r r r k k (29)
Substitution of Eq. ( 29) into Eq. ( 28), arrives

                              0 1 0 0 0 0 0 1 0 2 0 1 0 j j 0 0 1 0 2 0 1 0 2 2 0 j 0 0 H + , 2 = + , 2 = = p p v v k j w p p h d e j w p p h e d d k R e d r                                       kr kr kr r r r r r r r r r r k k k k r (30)
where

              3 1 1 2 2 2 0 1 p b p v j W Q P QW Q W R k          k k k k k k
, and the inverse Fourier transform function of

  v R k is given as     j = v v r R e d    kr r k k.
Combined with Eqs. ( 30) and ( 27), Eq. ( 28) can be written after the Fourier transform, as:

      1 2 w b p p Q P W Q Q    k k k (31)
where

    0 0 0 2 2 2 2 2 2 0 0 0 0 0 0 1 (e 1 ) 2 e ( e 1
)

jk H jk H jk H p w Q Qk Q c j j Qk                    k k , .
Similarly, by applying Fourier transform to Eq.( 5), one arrives at:

    3 3 p b P Q W  k k ( 32
)
where

2 2 2 3 0 0 p Q jρ ω k = - -k .
According to Eqs. ( 26) and ( 17), it is derived

              4 2 1 2 1 2 b 3 3 1 3 + 1 + p b p b D D m m Q Q G G W Q G P P           k k k k (33)
Substituting Eqs. ( 31) and (32) into Eq. ( 33), one obtains the displacement as:

      b = b W F  k k k (34)
where

    3 3 = 2 1 b p L F G Q QQ   k ,   3 3 3 = 1 L w m w p p Q Q G Q Q Q Q     and         0 0 2 4 2 2 0 0 0 0 + 1 1 1 jk H jk H wm p b Q D D j c Q e c e                     k , Γ
is the sum of surface densities of the two face plates, given as

p b m m    .
Given that the two faceplates are connected by the honeycomb core, the mass of the honeycomb should be reflected in the model. The total surface density  is thus modified by adding the surface density of the honeycomb core c m , c Γ Γ

c m = + ( 35
)
where the surface densities of the honeycomb cores are calculated by

             2 2 2 2 2 2
1. [START_REF] Bolton | Normal incidence sound transmission through double-panel systems lined with relatively stiff, partially reticulated polyurethane foam[END_REF]

                     (36)
where M and S are the unit cell mass and cross section area of the honeycomb cores respectively, c ρ is the density of the honeycomb cores.

Substitution of Eq. ( 34) into Eq. ( 27) yields the displacement of the bottom facelate:

    3 3 1 2 2 2 1 + + b b b b G Q w Q Q Q    r (37)
where

1 b Q , 2 b Q and 3 b Q are:               0 0 0 2 2 2 2 1 0 0 00 00 2 3 3 3 0 0 2 0 0 3 00 1 00 3 = 1 (e 1) + 1 1 1 1 2 + 2 jk H jk H jk H c b b b Q Q k j cQ e c e Q G Q G c j Q c j Q Q j c G j c Q Q                                        (38)
Similarly, Substitution of Eq. ( 31) into Eq. ( 27) leads to the pressure on the surface of the top faceplate:

      3 3 0 0 1 1 0 0 3 0 0 1 2 2 1 2 1 + 2 + + m b b b G Q Q j c Q p QG j c QQ j c Q Q Q Q                 r (39)
where

3 0 0 2 0 0 2 m Q G ρ c jωQ Q Q ρ c jωλ = - + + .
Combing Eqs.( 6), ( 14), ( 16), ( 17), ( 34) and ( 36) yields )

          1 1 2 1 = v b v v Q Q w Q Q p  r r r (40)
jk H jk H v Q c j j          .
According to Eq. ( 32), the sound pressure and velocity in the transmitted sound field are:

        3 00 3 b b p i c w v i w        r r r r (41)
Finally, the STL and SAC of the honeycomb sandwich with perforated faceplate can be obtained by comparing the transmitted and reflected sound energy with the incident energy, as:

here, i I , t I and r I are the total incident energy, the transmitted energy and the reflected energy, respectively, which can be calculated as:

                        i t 3

Validation

The proposed theory in the last section should be validated because of the For air inside the perforated pore, the thicknesses of viscous and thermal boundary layers have the same order of magnitude as the pore radius in the considered frequency range. Therefore, the thermoacoustic modulus is applied to calculate the sound pressure and temperature variation of the air, with both viscous and thermal losses included.

With air in the incident, transmitted and inside the core considered as non-viscous, the pressure acoustic modulus is applied to calculate the pressure and air velocity. At solidfluid interface, the velocity of air is equal to that of solid, and temperature variation is adiabatic at the interface. ). The sound absorption coefficient of the rigid perforated sandwich panel is then compared with that derived by Tang et al.'s theory [START_REF] Tang | Heterogeneously perforated honeycomb-corrugation hybrid sandwich panel as sound absorber[END_REF] which calculates the sound absorption of rigid frame microperforated sandwich panels as shown in Fig. 6. The geometrical parameters of the rigid microperforated sandwich panel with rectangular core are the same as that listed in Table 1. It is clear that the present theory agrees well with Tang et al.'s theory for the sound absorption prediction. hexagonal honeycomb sandwich panel in Fig. 7 are the same as that listed in Table 1 except from the perforation ratio and pore diameter. It can be seen from Fig. 7 that STL by the present theory agrees well with that by Kumar et al.'s model.

Parameter study

To figure out the effects of perforated faceplate and explore the influential parameters related to the perforation, systematic parameter studies based on the validated analytical model are performed from the viewpoint of STL and SAC. For further validation of the model, corresponding results obtained using FE models are also presented below.

Effect of perforated faceplate

The STL and SAC of hexagonal sandwich panels with perforated faceplates are compared with those of hexagonal sandwich panel without perforation in Fig. 8.

Geometrical parameters of the sandwich are the same as those listed in Table 1.   Figure 8 (a) shows perforation-induced increment of STL within the frequency range of 700~1200 Hz. The peak frequency in the STL curve is identical to that in the SAC curve, which means that the enlargement of STL should be attributed to the appearance of SAC. For the sandwich without perforation, since no acoustic energy can be consumed during sound propagation, the STL is decided by the reflection of sound wave. In the presence of perforation, sound wave enters the sandwich via the perforated pores and the acoustic energy is consumed due to viscous and thermal losses inside the pores.

0 0 1 860Hz 2 tot c f H h       (44)

Influence of perforation ratio

Figure 9 shows three hexagonal sandwich panels having identical geometrical parameters but different perforation ratios. These sandwich panels have one, two and three pores in each unit cell of the faceplate as shown in Figs. 9. All the pores have the same diameter of 0.5 mm, and the other geometrical parameters of the sandwich panels are the identical to those of Table 1. Accordingly, the perforation ratios are 0.39%, 0.79% and 1.2%, respectively. The influence of perforation ratio on the STL and SAC of hexagonal sandwich panels is displayed in Fig. 10. It can be seen from Fig. 10 that with the increase of perforation ratio, the peak frequency of the STL and SAC increases, which can also be seen evidently from Eq. ( 41). Besides, the bandwidth for SAC is enlarged by the increase of perforation ratio. As the perforation ratio is increased, the viscous and thermal losses inside the perforated pores are enhanced as a result of the increased contact area between air and solid frame, thus enlarging the resistance of the perforated faceplate. The enlarged resistance will increase the bandwidth [START_REF] Maa | General theory and design of microperforated-panel absorbers[END_REF].

Influence of pore diameter

Figure 11 shows three hexagonal sandwich panels having the same geometrical parameters except the pore diameter and number. These sandwich panels have one, two and four pores in each unit cell of the top faceplate. With identical perforation ratio assumed, the corresponding pore diameters are 1 mm, 0.707 mm and 0.5 mm. The remaining parameters are the same as those listed in Table 1. 41), the peak frequency of SAC increases with decreasing pore diameter and, accordingly, the peak frequency of STL increases. Besides, with the decrease of pore diameter, the bandwidth of SAC is enlarged. Decreasing the pore diameter increases the contact area between air and solid frame inside the pores, which in turn leads to enlarged resistance of the perforated faceplate.

Influence of core configuration

According to the theory presented in Section 3, the surface density and perforation ratio of a honeycomb sandwich panel are affected by core configuration. Consequently, the STL and SAC of the sandwich are also affected, as discussed below.

Let sandwich panels with different honeycomb cores have the same geometrical parameters and the same effective mass. When the geometrical parameters of hexagonal sandwich panel are the same as shown in Table 1, the side lengths of rectangular and triangular cores can be calculated using Eq. ( 21). Table 2 presents the side length and perforation ratio for each type of sandwich panel. For simplification, the length of the rectangular core is set as s s a b = .

Table 2. Side lengths of honeycomb cores and perforation ratios of sandwich panels with these cores The STL and SAC of the three sandwich panels are compared in Fig. 13. It is seen that the peak frequency increases as the edge number of the core is increased, and sandwich panel with triangular core has the best acoustic properties at relatively low frequencies.

Conclusion

An analytical model for estimating the STL and SAC of honeycomb sandwich panels with perforated faceplates is developed by taking into account the effect of faceplate perforation as well as the effect of core configuration.

Fig. 1 Fig. 2

 12 Fig. 1 Schematic of surface-perforated sandwich panel with hexagonal core

  , p b m are the surface densities of the faceplates, , p b D denote flexural rigidities, and   , p b p  r are the pressure differences.

  the displacements of the two faceplates when they are excited by an unit force    r , one has:

  simplifications and assumptions made to develop the model. To this end, a FE model is established based on the commercial software COMSOL Multiphysics. The calculation unit cells of sandwich panels with perforated facing are shown in Fig.3for hexagonal core, rectangular core and triangular core. The shapes of these unit cells are all rectangular, so that periodic boundary conditions can be added to the boundary of each unit cell. The calculation model of hexagonal sandwich panel is shown in Fig.4, where two perfectly matched layers are added to the ends of incident and transmitted sound fields to mimic open and non-reflecting infinite sound fields. The solid mechanics modulus of COMSOL is applied to calculate the displacement and velocity of the solid part of each sandwich panel, with the sandwich regarded as an isotropic elastic material.

Fig. 3 Fig. 4 FEFig. 5

 345 Fig. 3 Unit cells of honeycomb sandwich panels with perforated faceplates for FE simulation

Fig. 6 37 ]Fig. 7

 6377 Fig.[START_REF] Bolton | Sound transmission through multi-panel structures lined with elastic porous materials[END_REF] Comparison of SAC between the present model and results by Tang et al.[START_REF] Tang | Heterogeneously perforated honeycomb-corrugation hybrid sandwich panel as sound absorber[END_REF]

Fig. 8

 8 Fig. 8 Comparison of sound transmission loss and absorption coefficient between sandwich panels with non-perforated and perforated faceplates

  correction. The results of Fig. 8 (b) demonstrate that the peak frequency in the SAC curve as predicted by the present analytical model is approximately equal to the resonance frequency estimated by Eq. (41).

Fig. 9 Fig. 10

 910 Fig. 9 Schematic of hexagonal sandwich panels with different perforation ratios

Fig. 11 Fig. 12

 1112 Fig. 11 Schematic of hexagonal sandwich panels with different pore diameters but fixed perforation ratio

Fig. 13

 13 Fig. 13 Influence of core configuration on sound transmission loss and absorption coefficient of honeycomb sandwich panels having identical effective mass

  The reflected and transmitted sound pressures are expressed by applying the Green's function and solved by employing Fourier transforms. The STL and SAC are obtained by comparing the reflected and transmitted sound energy with the incident sound energy. FE models are developed to validate the analytical model, with good agreement achieved. In the analytical model, the two face plates are assumed to have the same displacement because of the honeycomb connection, which makes the presented model valid for frequencies lower than faceplate resonance frequency. Acoustic properties of frequencies higher than the faceplate resonance frequency go beyond our investigation in the present model but are part of our following research project. Influences of faceplate perforation, perforation ratio, pore size, and core configuration are discussed using the analytical model. Results show that perforation in faceplate can improve the STL and SAC at low frequencies, and the peak frequency in the STL and SAC curves increases with increasing perforation ratio and decreasing pore size. Compared with other sandwich panels of the same effective mass and perforated pores, the sandwich panel with triangular core exhibits the lowest peak frequency for both STL and SAC. Results of the presented paper can inspire researchers to design multifunctional lightweight sandwich structures with superior mechanical and acoustic properties by artificially adding perforations to existing sandwich panels or optimizing the sandwich core.

Table 1 .

 1 Geometrical parameters of perforated honeycomb sandwich panels for FE

								simulation
								Hexagonal core
	1 h =	1 mm	, 1 2 mm h =	,	H =	17 mm	,	a =	6.2 mm	,
			t =	0.2 mm	,	d =	1 mm	,	σ =	0.79%
							Rectangular core
	1 h =	1 mm	, 1 1 mm h =	,	H =	20 mm	,	r a =	12 mm	,
	r b =	10 mm	,	t =	0.3 mm	,	d =	1.2 mm	,	σ =	0.94%
							Triangular core
	1 h =	1 mm	, 1 2 mm h =	,	H =	17 mm	,	t a =	10 mm	,
			t =	0.2 mm	,		d =	0.8 mm	,	σ =	1.16%

Acknowledgements

H. Meng was sponsored by China Scholarship Council as a joined Ph. D. student in Ecole Centrale de Lyon. This work was supported by the National Natural Science Foundation of China (51528501 and 11532009) and the Fundamental Research Funds for Central Universities (2014qngz12). This work was performed within the framework of the Labex CeLyA of Universite de Lyon, the program Investissements d'Avenir (ANR-10-LABX-0060/ ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).


Mohammed Alshaer

Yehia Taher

Rafiqul Haque

Mohand-Saïd Hacid

Mohamed Dbouk

IBRIDIA: A Hybrid Solution for Processing Big Logistics Data

Keywords: Realtime Processing, Clustering, Big Data, Internet of Things, Logistics, Hierarchical Clustering Algorithm

Internet of Things (IoT) is leading to a paradigm shift within the logistics industry. Logistics services providers use sensor technologies such as GPS or telemetry to track and manage their shipment processes. Additionally, they use external data that contain critical information about events such as traffic, accidents, and natural disasters. Correlating data from different sensors and social media and performing analysis in real-time provide opportunities to predict events and prevent unexpected delivery delay at run-time. However, collecting and processing data from heterogeneous sources foster problems due to variety and velocity of data. In addition, processing data in real-time is heavily challenging that it cannot be dealt with using conventional logistics information systems. In this paper, we present a hybrid framework for processing massive volume of data in batch style and realtime. Our framework is built upon Johnson's hierarchical clustering (HCL) algorithm which produces a dendogram that represents different clusters of data objects.

Introduction

Lately, with the advent of the Internet of Things (IoT), the operational 2 landscape of the logistics industry is changing. Today, logistics companies (such as DHL 1 and FedEx2 ) use various sensors for tracking delivery, maintaining sensitive products, and many other purposes. Sensors assist in tagging and connecting factories, ships, and machines. They also allow handling real-time events. Additionally, connectivity of "things" enables instant communication between devices via the Internet [START_REF] Braun | IoT in the Supply Chain -Inbound Logistics[END_REF]. This hyper-connected ecosystem promises far-reaching payoffs for logistics operators, their business customers, and end-customers [START_REF] Macaulay | Internet of Things in Logis-tics[END_REF]. One of the major advantages of IoT-based ecosystem is that it enables connecting the logistics sensors with external sensors such as weather sensors and traffic (GPS) sensors, etc. Furthermore, IoT enables connecting with social media such as Twitter which very often provides important traffic information tweeted by the users. The sensors and social media produce information about events such as accident, weather, natural hazards, heavy road constructions, etc. which are critical to logistics companies. These information can be used to carry out some critical analysis such as predictive analysis to forecast shipment delay or prescriptive analysis to optimize routes to guarantee in-time delivery which increases customer satisfaction and hence guarantees customer retention.

Although many solutions were proposed in the last two decades within logistics domain to tackle various problems, delivery delay remained an open issue. Timely delivery is a huge challenge for logistics companies because sometimes delays are caused by factors outside of anybody's control. Delay has various impacts such as, customer churn or cancellation of orders which eventually leads to huge losses. Therefore, timely delivery is critically important to logistics companies.

In recent years, logistics companies have started to investigate how to exploit data in predicting delay. The data driven prediction of delay is gaining popularity. Especially, with the advent of Big Data technologies, the logistics providers are focusing heavily on using streams of events such as accident, high-traffic stemming from external sources such as social media to perform analysis and predict delay in real-time. The real-time prediction of delay enables companies to pro-act such as optimizing route on the fly in (nearly) real-time. We have investigated the requirements of a real-time system which can perform analysis and predict delay. The core requirements are: ability to collect logistics data in real-time from multiple heterogeneous sensors, social media, and business processes; ability to process data efficiently in real-time or batch-style; a model for analyzing data for predicting the delay; and a model which produces an optimal routing plan to prevent the predicted delay. However, since data is the key element of analysis, efficient processing of data to produce quality dataset is a sine qua non. In this paper, we focus on developing a hybrid solution which enables efficient processing of data in realtime and batch style. It is worth noting that this paper is an extension of our previous work [START_REF] Alshaer | Prolod: An efficient framework for processing logistics data[END_REF] where we developed batch style data processing. In this paper, we added functionalities that enable real-time processing of data.

Realtime processing is strongly required to enable logistics service providers to perform analysis in realtime.

Existing data processing approaches (e.g., techniques or algorithms) are not adequately efficient to process data in real-time. The existing solutions are built on classical data processing techniques. Therefore, conventional logistics information systems are not able to process sensor or social media data in real-time because these data flow with high velocity [START_REF] Taxidou | Realtime analysis of information diffusion in social media[END_REF]. Additionally, the traditional data processing approaches are not able to deal schemaless data such as text. In the following, we explain the data variety and velocity challenges:

• Variety denotes different types of data models such as structured (e.g., data stored in relational tables) and semistructured (e.g., JSON and XML). Also, data may not have any structure such as text. Processing such a wide variety of data is heavily challenging and conventional information systems are not ready to tackle the variety challenge. Additionally, modern technologies have limited capabilities to tackle variety challenge. For instance, we conducted an experimental study with a solution called "Massive Online Analysis" (MOA 3 ) which is an advanced version of one of the most widely used machine learning solution called WEKA 4 . We found that it cannot load tweets (which are texts). The shortcoming of existing solutions leave one very important question to logistics companies: How to handle different data representations? Moreover, variety hinders data integration [START_REF] Nieva | Integrating heterogeneous data[END_REF][START_REF] Botta | Integration of cloud computing and internet of things: a survey[END_REF][START_REF] Mezghani | A semantic big data platform for integrating heterogeneous wearable data in healthcare[END_REF]. Data integration is of critical importance within Big Data because it enables correlating events stemming from heterogeneous sources and enables predicting delay with higher accuracy.

• Velocity refers to the speed at which data flow within and across the Web. Unlike the earlier days, data now-a-days is in motion. Millions of records may result in a millisecond. Sensor and social media data move with high speed. There are solutions which can be used to collect high-speed data yet processing them in real-time is heavily challenging.

In our solution called ProLoD [START_REF] Alshaer | Prolod: An efficient framework for processing logistics data[END_REF], we focused on variety challenge. In this paper, we extended ProLoD to tackle the velocity challenge by adding functionalities for collecting and processing data streams in real-time. With this extension, our solution named IBRIDIA has turned into a hybrid solution that is able to process data in both real-time and batch style. IBRIDIA relies on extended hierarchal clustering algorithm proposed in [START_REF] Power | Finding fires with twitter[END_REF] for processing data streams flowing at high-speed. The core contribution in this paper include the following:

• Developing a data streamer which fetch logistics data streams from sources such as social media and sensors.

• Building a real-time data processing engine that relies on extended agglomerative hierarchical algorithm.

The remainder of the paper is organized as follows. In Section 2, we will present a motivating scenario. In Section 3, we will describe the problem we are trying to solve more clearly. In Section 4, we present the work related to our research. Our previous work ProLoD and the extension of ProLoD which is the realtime data processor RePLoD is discussed in Section 5 by presenting the overall solution IBRIDIA. In Section 6, we present briefly the implementation of IBRIDIA. We showed the results of several experiments between the two data processing components ProLoD and RePLoD in Section 7. We conclude the work in Section 8.

Motivating Scenario

There are different modes of shipment used by logistics service providers including air cargo, ships, and ground cargo (e.g., Lorries and trucks). A single mode of transportation may not be adequate to deliver goods. Especially, a cross-border long-running shipment may include several modes of transportation. Consider a case where a product manufactured in China will be shipped to different customers located in different cities in the United States; the shipment process has to be multi-modal which means that the process will include lorries, trucks, train, ship or air etc. (Figure 1).

The integrated multi-modal logistics processes are prone to encounter various challenges namely delivery delay. For instance, the shipment could be delayed if clearance at the port is delayed, even if all other modes of transportation meet pre-defined schedule. Uncertain events such as natural disaster, war, strike, protest may affect one or more of the delivery modes at one or more steps of the integrated logistics processes. Uncertainty is the major challenge concerning such events. Therefore, pro-activeness to the best of our knowledge is a suitable approach which needs continuous streaming of data that contains information of events that may lead to delivery delay. In other words, realtime analysis of data to extract information of events which may lead to delivery delay.

Problem Description

There are different challenges involved in an integrated mission-critical logistics process. The predominant challenges reported by experts include the followings: in-time delivery, cost optimization, efficient management of intermodal transportation, transferring information, Security, and Infrastructure [START_REF]LogisticsPlus, Top Logistics Challenges Facing Shippers Today[END_REF][START_REF] Morales | Logistics & Transportation Executives Facing Todays Challenges, Seek Solutions Well into the Future[END_REF][START_REF] Němec | Distinguished problems of logistics[END_REF]. However, in-time delivery is one of the key performance indicators (KPIs) of logistics services. Delay of a scheduled (expected) delivery increases customer dissatisfaction. In order to prevent delay, logistics service providers heavily rely on automated solutions. Business intelligence is a widely used solution that enables performing different types of cycle time analytics [START_REF] Nwaubani | Business intelligence and logistics[END_REF] that analyze delay for different combinations of goods, routes, modes and weather condition.

However, this is a reactive approach which performs analysis on historical data. In other words, traditional business intelligence especially, BI&A 1.0 and BI&A 2.0 use only internal data which stem from different information systems and legacy systems [START_REF] Chen | Business intelligence and analytics: From big data to big impact[END_REF]. Also, the process mining tool PRoM [START_REF] Van Dongen | The prom framework: A new era in process mining tool support[END_REF] -a recent tool for mining business processes -lacks the ability to exploit external data. Consequently, the analytics misses important external data such as sensor data (for example, global positioning systems (GPS) data) and social media data (such as Twitter data). The advent of Big Data technologies created wide opportunities to exploit such external data which enhances the predictability of analytics.

More specifically, these data are effective to forecast potential delivery delay as they contain important information such as high traffic, weather report, political events such as protest, and other events such as unexpected natural disasters (e.g., Earthquake). However, collecting, cleaning, filtering, integrating, and storing data from heterogeneous sources is a non-trivial task. Particularly, a seamless integration of unstructured text sourcing from Twitter with structured business process data is not possible by existing logistics solution frameworks. Dong et al. [START_REF] Dong | Big data integration[END_REF], outlined several Big Data integration challenges. Furthermore, there are several techniques and approaches for processing data, however, our investigation suggests that there is a scope to improve these techniques specifically the clustering algorithms.

In this paper, we aim to address the two problems discussed in the above: data preparation and data processing. The goal is to provide efficiently processed data streams which are employed to perform an effective analysis.

To that end, we developed IBRIDIA that enables to fetch data from various sources, pre-process and process tasks in real-time.

Related Work

Clustering data in real time has drawn a huge research interests in the recent past, especially with an extensive demand of using analytics on streaming data. We choose clustering because we needed to work with unlabeled data which means no model training data set is available and we know nothing about the data previously, thus we needed an unsupervised learning model. A few initiatives have been taken in the field where BIRCH (balanced iterative reducing and clustering using hierarchies) was one of the basic methods for data stream clustering [START_REF] Birch | an efficient data clustering method for very large databases[END_REF]. Essentially, BIRCH introduced micro and macro clustering as two new concepts, and was fabricated to work with traditional data mining techniques but not with voluminous amounts of data sets like data streams. Later, the STREAM algorithm proposed by Guha et al. [START_REF] Guha | Clustering data streams: Theory and practice[END_REF][START_REF] Guha | Clustering data streams[END_REF] was an extension of classical K-median and the first algorithm known with the ability to perform clustering on entire data streams.

In [START_REF] Babcock | Maintaining variance and k-medians over data stream windows[END_REF], Babcock et al. suggested the sliding window model as an extension to STREAM and thus they changed the concept from one single pass over the data, to the concept of receiving data points as a stream and taking into consideration the points that fall within a specific range representing the sliding window. The CluStream framework was suggested by Aggarwal et al. [START_REF] Aggarwal | A framework for clustering evolving data streams[END_REF] and it was considered effective in handling data streams; it divides the clustering process into two components: online and offline. The former periodically uses micro clusters to store detailed summary statistics, and the latter uses the summary statistics to produce clusters. Later, Aggarwal et al. [START_REF] Aggarwal | On high dimensional projected clustering of data streams[END_REF] suggested HPStream that works on data streams high dimensionality reduction by means of data projection prior to clustering. Denstream algorithm was proposed in [START_REF] Cao | Density-based clustering over an evolving data stream with noise[END_REF] as an extension for DBSCAN where they combined micro clustering concept to the density based connectivity search.

Another density-based extension is the D-Stream proposed in [START_REF] Chen | Density-based clustering for real-time stream data[END_REF]. The proposed solution maps each new data point to a specific grid upon its arrival; the density information is stored and then clustering is applied to the density data grids. Khalilian et al. [START_REF] Khalilian | K-means divide and conquer clustering[END_REF] suggested an improvement for well-known K-Means algorithm. They applied the widely-known divide and conquer method that is capable of clustering objects with high quality and efficiency. Specifically, the solution is suitable for analyzing high dimensional data, but not for realtime data streams. EStream [START_REF] Udommanetanakit | E-stream: Evolution-based technique for stream clustering[END_REF] is a data stream clustering technique, which supports five types of evolution in streaming data. They are as follows: appearance of new cluster, disappearance of an old cluster, split of a large cluster, merging of two similar clusters and changes in the behavior of cluster itself. It uses a decaying cluster structure with a histogram to approximate the streaming data. Although the algorithm has the disadvantage of needing an expert intervention to specify many parameters before it works, its performance is better than HPStream algorithm [START_REF] Mahobiya | Performance comparison of two streaming data clustering algorithms[END_REF].

In [START_REF] Cheng | Fast fuzzy clustering[END_REF], a multi-level unordered sampling technique was suggested to boost the time performance of fuzzy C-means. The technique is double phased. In the first phase, the random sampling is applied to estimate centroids and then fuzzy C-means "FCM" is performed on the full data with the previously initialized centroids. Fuzzy C-means together with probabilistic clustering were then extended to work on huge data sets by the sampling based proposal of Richards and James [START_REF] Hathaway | Extending fuzzy and probabilistic clustering to very large data sets[END_REF]. In [START_REF] Cannon | Efficient implementation of the fuzzy c-means clustering algorithms[END_REF], an algorithm called AFCM was suggested to speed up FCM. This is done using lookup table. In [START_REF] Wu | Designing scalable and efficient parallel clustering algorithms on arrays with reconfigurable optical buses[END_REF], the authors proposed several efficient and scalable parallel algorithms for a special purpose architecture description of a modified FCM algorithm known as 2rFCM. A fast FCM algorithm was proposed in [START_REF] Al-Zoubi | A fast fuzzy clustering algorithm[END_REF]. They employed the concept of decreasing the number of distance calculations by checking the membership value for each point. Furthermore, many machine learning libraries are used to implement the algorithms discussed above. We adopt building a connectivity model (hierarchical clustering algorithm) in our framework. The three main reasons for our decision are the following:

1. No prior knowledge of the nature of the coming data (format, structure, features, etc.) 2. No prior knowledge of how many categories can the data be classified into (number of clusters is unforeseen) 3. The clusters probably will evolve with time (keep changing dynamically, i.e., creating, removing, splitting and merging clusters).

Since we are interested mainly in hierarchical clustering, we studied the machine learning libraries within the scope of this algorithm. Datumbox [START_REF] Vryniotis | DatumBox machine learning framework[END_REF] is a robust framework that provides different functions like Sentiment Analysis, Twitter Sentiment Analysis, Subjectivity Analysis, Topic Classification, Language Detection, Keyword Extraction, Text Extraction and Document Similarity. At low-level, the basic machine learning algorithms such as K-means, hierarchical clustering, and classification algorithms perform the above functionalities. Although this library is very powerful in handling different data types (categorical, numerical, etc.), it was not implemented to work in the environment of streaming data. Therefore, it can read bulk data. Apache Spark [START_REF] Meng | Mllib: Machine learning in apache spark[END_REF] is a fast-general-purpose cluster computing system. It supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming, For clustering, Spark offers limited features in particular; it supports few algorithms such as K-Means. The library is missing hierarchical clustering algorithm, which we found suitable for our research project.

Furthermore, the library offers the streaming K-means; it is applicable on numerical data only which is a limitation for Big Data where data variety is major challenge. SPMF [START_REF] Fournier-Viger | Spmf: a java open-source pattern mining library[END_REF] offers implementations of 120 data mining algorithms for association rule mining, item set mining, sequential pattern mining, and of course clustering and classification. It works only with numerical data and this was mentioned explicitly in the documentation. The input is a set of vectors containing double values only, a parameter "maxdistance" and a distance function. This implies the same limitation that Spark has. To the best of our understanding, this shortcoming is obvious because, the clustering is usually done according to Euclidean or Manhattan distance functions that need numerical data to be applied.

WEKA [START_REF] Hall | The weka data mining software: an update[END_REF] is a widely-known library. It is an integrated system which consists of a collection of machine learning algorithms for data mining tasks.

The algorithms can either be employed directly for a dataset or called from within a Java code. WEKA contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is a well-suited solution for developing new machine learning schemes. It is very efficient and can be used with big data analytics but needs to work only on data at rest and with a specific file format called ARFF. Recently, an initiative has been taken to extend WEKA to be used for mining data Streams.

MOA (Massive Online Analysis) [START_REF] Bifet | MOA: Massive online analysis[END_REF] is an open source library for data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, and concept drift detection and recommender systems) and tools for evaluation. MOA provides approximately all clustering algorithms for streaming data but it is confined to a specific format just like WEKA (ARFF file only). It enables to generate synthetic data streams and allows users to visualize data clustering in real-time.

Waller and Fawcett [START_REF] Waller | Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management[END_REF] underline the importance of data and analytics for supply chain management (SCM). They introduce the term "SCM data science", referring to Big Data Analytics (BDA), as the "application of quantitative and qualitative methods from a variety of disciplines in combination with SCM theory to solve relevant SCM problems and predict outcomes, taking into account data quality and availability issues". Bi and Cochran [START_REF] Bi | Big data analytics with applications[END_REF] argue that BDA has been identified as a critical technology to support data acquisition, storage, and analytics in data management systems in modern manufacturing. They attempt to connect IoT and BD to advanced manufacturing information systems to help to streamline the existing bottlenecks through improving forecasting systems. Similarly, Gong et al. [START_REF] Gong | Information and decision-making delays in mrp, kanban, and conwip[END_REF] argue that a production control system (PCS) can be considered an informationprocessing organization (IPO).

Discussion

The solutions and tools mentioned in the state of the art provide a variety of machine learning algorithms that can be used for predictive analytics tasks, such as feature selection, parameter optimization and result validation. Many of these systems offer basic visualizations including residual plots, scatter plots and line charts. However, the visualization feature of these systems is limited to presenting the final results; they do not offer any interactive means for manipulation, feature selection or model refinement; instead, these systems often opt to show baseline models or simple statistical measures for result validation, working as more of a black-box system. SPMF and Spark worked only on numerical data. Additionally, we found that Spark's MLib library does not have an implementation of hierarchical clustering. Datum box had a specific structure for storing and processing data and it lacks the ability to read data by lines. It can handle data as a batch that can be read in one go and thus it was not suitable for real time environments.

WEKA developers extended it into MOA, the version that fits real time processing but it is still very recent and thus had some code bugs that did not allow us to benefit from it 5 . Besides that, MOA is locked into a specific file format which is ARFF and hence it is unable to read other data format. An ARFF format needs to have attributes, types, and data explicitly mentioned within the file. Nevertheless, in our case the data streamed and fed into the algorithm do not necessarily have an attribute. Rather, data could be a set of records each of which is made up of different text words. Therefore, we decided to write our own implementation.

In order to fulfill the needs of the logistics sector, we can use social media to do the data enrichment and combine the data from multiple sources to validate and analyze the current situations. But using social media alone, might not be of major interest, there is a need to build a new model that is enriched from social media and the different sensors to predict the delay for the delivery process and suggest improvement according to it. Even, after predicting the delay, more work should be done using advanced analytics for the optimization of the route planing algorithms. The state of art was missing the data integration to forming up the convenient model from the different data sources which have different data presentations (such as text, JSON, XML, audio, video, etc.) In summary, considering the evaluation of data sources, most of the existing solutions are confined to one data source for analytics and prediction.

Additionally, for realtime systems with continuous improvement, the majority of researches used large static historical datasets for their testing while our approach does not depend on historical data alone.

IBRIDIA -Solution Overview

In this section, we describe the two main modules of IBRIDIA. In logistics system, we have data generated from internal system for stock, orders, shipments, etc. Also, there is a need to collect and analyze data from external sources in realtime especially to monitor the different statuses of the delivery. To address both needs, within IBRIDIA, we developed a batch style data processing engine that we called ProLod and a realtime data processing engine that we called RePLoD. We explain these two modules in the following subsections. Then, we describe the data processing model that IBRIDIA relies on for both realtime and batch style data processing. Figure 2 depicts a high-level architecture of IBRIDIA.

In IBRIDIA there are four components: data streamer, the storage, batch style data processing engine, and real-time data processing engine. The data streamer fetch data from internal and external data sources and ingest them into realtime processing engine and storage. It is worth noting that we used filtering, etc. In case of realtime processing, the streamer sends data directly into the processing engine (RePloD) which carries out processing tasks in realtime. In the following, we provide more detail about the data processing engines.

ProLod -Batch Style Data Processing Engine

ProLoD represents the batch-style processor for processing of logistics data stemming from multiple heterogeneous sensors (that include vehicle sensor, weather sensor, etc.), logistics applications, microblog (e.g., Twitter), and social media (e.g., Facebook). ProLoD comprises two phases: data preparation phase and processing. The former consists of data extraction (collection), data cleansing, data filtering, data integration, and data storage. In the latter phase, well-prepared data are clustered. Figure 2 shows different functionalities of these phases. ProLoD relies on different machine learning techniques specifically the clustering techniques for data processing.

ProLoD includes five components: data extractor, data cleaner, data filter, data integrator, and data storage for performing data preparation functions.

It has a data processor which performs clustering in the second phase.

RePLoD -Realtime Data Processing Engine

RePLoD represents the core component in our framework for processing the realtime data. As the speed of events from sensors and social media increases, it creates an emerging need for fast processing known as stream processing mechanism. Events may lead to catastrophic consequences if not handled properly in-time. RePloD was designed to add the missing functionalities to the system by adding a convenient way for handling the events generated in realtime. These events are first enqueued into the memory through distributed messaging system. The memory was used instead of the disk because its access speed is faster than the disk by 100,000 times. In this way, we prevent any overwhelming of the receiver and we guarantee fault tolerance in case of any failure. This added feature prevents the loss of any of the data due to their fast generation. Batch processing is not always the right way to do it, sometimes it is important to do the processing on the fly as soon as the events arrive to the servers. These cases can be faced in real world scenarios such as accidents occurring now on roads, bad weather, maintenance of buildings which need to be notified for the driver in realtime to prevent the catastrophic effects due to delay in delivery. These facts carried us to extend the processing behavior to be able to do the required processing in realtime without doing it in batches. RePLoD performs clustering of the events in realtime and gets immediate insights over the processed data.

Data Preparation Tasks

Both ProLoD and RePloD perform five data preparation tasks using two different approaches namely batch style and realtime respectively. These tasks are explained in the following.

• Data Extraction: It is the systematic approach to gather and measure information from a variety of sources to get a complete and accurate picture of an area of interest. The data extractor works with both internal and external sources of data. The internal data sources are typically the information systems used by the users. Consider a user that has an information system consisting of a supply chain management (SCM), a customer relationship management (CRM), a logistics management system, and an account management system (AMS). These systems produce a large amount of data that are collected by the data extractor. It also fetches data from external sources such as Twitter, traffic sensors, weather sensors, Facebook and other social medias. In addition, IBRIDIA's processing components extract archived sensor data of completed logistics processes. In most of the cases, we found that data extraction from internal sources is more trivial than external ones. Additionally, internal data were transferred faster than the external ones.

IBRIDIA can collect structured and unstructured data. For instance, it collects unstructured texts from Twitter, and Facebook and structured business process data from logistics information system.

• Data Filtering: It refers to a wide range of strategies or solutions for refining data sets. Datasets are refined into simply what a user (or set of users) needs, without including other data that can be repetitive, irrelevant or even sensitive. ProLoD and RePLoD aim to eliminate all possibilities of data overloading which can increase computational cost and effort during data processing and may jeopardy the analysis regarding accuracy. They collects data that are related to logistics and specifically the data chunks whose hashtags (the words prefixed by #) determine direct and indirect connections with transportation, delivery, logistics, shipment, etc. Consider the term "protest" which may be a political protest or else but can have a great impact on delivery of goods and hence can delay the delivery. However, consider a tweet "the New York stock prices are extremely high today" which will be removed by the data filter because it does not carry any information related to logistics processes.

• Data Cleaning (i.e. data scrubbing): It is the process of detecting and correcting (or removing) corrupted or inaccurate records from a record set, table, or database. ProLoD and RePLoD clean data from all unwanted symbols, numbers, stopping words, hashtags, and any other data items that might lead to noise and cause inaccuracy. Figure 3 shows an example of cleaning Twitter data using ProLoD.

• Data Integration: In IBRIDIA, data integration is performed in two steps. In the first step, the data are transformed from source to target serialization format. Currently, the target format is CSV. The second step is merging the transformed data. • Data Storage: This step aims to deal with the storage of the integrated datasets. After preparing the integrated datasets, ProLoD and RePLoD store data into the storage.

Data Processing Model

As mentioned earlier, IBRIDIA relies on the data processing model which we developed in our previous work [START_REF] Alshaer | Prolod: An efficient framework for processing logistics data[END_REF]. We explain the data processing model in this section. Choosing techniques or methods for developing the model is not a trivial job. There is an exhaustive list of techniques available from machine learning, data mining, and statistics. In our case, we considered the nature of data and operation styles to choose the right technique for building data processing model. Our data processing model relies on unsupervised learning techniques [START_REF] Michalski | Machine learning: An artificial intelligence approach[END_REF]. Unsupervised learning is a machine learning approach in which a system only receives input (x 1 , x 2 ,..., x n ) without any corresponding (supervised) output (which is also called labeled output).

Clustering and dimensionality reduction are the two most well-known unsupervised learning techniques. We choose clustering for our model because the objective function is expected to produce a clustered dataset which facil-itates efficient analysis in prediction of delivery delay. Clustering is a process of grouping or segmenting data items that are similar between them in a cluster and dissimilar to the data items that belong to another cluster [START_REF] Michalski | Machine learning: An artificial intelligence approach[END_REF].

There are different types of cluster models which are grouped into Connectivity models, Centroid model and Distribution models, Density models, Subspace model, Group model, and Graph-based models [START_REF] Estivill-Castro | Why so many clustering algorithms: a position paper[END_REF]. We are interested in techniques used for building connectivity model which fits to our objective more than the others. Hierarchical clustering is a widely used approach for building connectivity model based on distance connectivity between the data items. It is a process of producing a sequence of nested cluster ranging from singleton clusters of individual points to an all-inclusive cluster [START_REF] Cios | Data mining and knowledge discovery[END_REF]. The hierarchy of the clusters are graphically represented by a dendogram [START_REF] Galili | dendextend: an r package for visualizing, adjusting and comparing trees of hierarchical clustering[END_REF]. There are two approaches to develop a hierarchical cluster model:

• Agglomeration refers to an approach that start with the points as individual clusters and, at each step, merge the closest pair of clusters. It is also known as Bottom-Up approach.

• Divisive refers to an approach that starts with one, all-inclusive cluster and, at each step, splits a cluster until only singleton clusters of individual points remain. It is also known as Top-Down approach.

We found agglomerative hierarchical clustering approach for our solution because the bottom up approach is more flexible than the others in terms of choosing the number of clusters. The algorithm groups data one by one based on the nearest distance measure of all the pairwise distance between the data points. The distance between the data points is recalculated iteratively.

However, the choice of distance to consider for grouping data points is a critical matter. Several methods are available to address this question. These methods -found in [START_REF] Everitt | Cluster analysis[END_REF] -are summarized in the following:

Definition 1. Single-linkage: d(C i ,C j ) = min x∈C i ,x ∈C j d(x,x ).
It is equivalent to the minimum spanning tree algorithm [START_REF] Graham | On the history of the minimum spanning tree problem[END_REF]. One can set a threshold and stop clustering once the distance between clusters is above the threshold.

Single-linkage tends to produce long and skinny clusters. Definition 2. Complete-linkage: d(C i ,C j ) = max x∈C i ,x ∈C j d(x,x ). Clusters tend to be compact and roughly equal in diameter.

Definition 3. Average distance: d(C i , C j ) = x∈C i ,x ∈C j d(x,x ) |C i |•|C j | . Definition 4. Wards method d ij = d({X i }, {X j }) = X i -X j 2 is the sum of squared Euclidean distance is minimized.
The iteration is continued by grouping data items until a cluster is formed.

As mentioned earlier that the clusters are presented graphically by a dendo- • Step 6 : Repeat until ONLY one cluster remains.

gram
In [START_REF] Alshaer | Prolod: An efficient framework for processing logistics data[END_REF], we reported several disadvantages of the basic agglomerative clustering algorithm . In particular, undoing is not allowed and the time complexity is O(n 2 log n) where n denotes the number of data points. For a large dataset, the performance with respect to processing time may not be satisfactory. Based on the type of distance matrix chosen for merging, different algorithms may have one or more of the following drawbacks: (i) sensitivity to noise and outliers, (ii) partitioning a large cluster, (iii) difficulty in handling different sizes of clusters and handling convex shapes. In this algorithm, no objective function is directly minimized. Furthermore, in some cases identifying the correct number of clusters by the dendogram can be very difficult. Therefore, the basic algorithm agglomerative clustering algorithm is not suitable for clustering data. Hence, we choose extended agglomerative hierarchical algorithm proposed in [START_REF] Power | Finding fires with twitter[END_REF]. We intend to use the hamming distance as a measuring criteria in our algorithm, because it can be used as •

Step 1: Read new data streams.

• Step 2: Put the unique items in the vector format.

• Step 3: Fill a matrix of absence and presence of items.

• Step 4: Calculate hamming distance.

• Step 5: Update the distance matrix.

• Step 6: Create Cluster using minimum distance.

• Step 7: Repeat until only one cluster remains.

In what follows we explain the above steps using an example where we illustrate how IBRIDIA data processing model works. It begins with reading records. Since data is read from the first row, thus the attribute names do not exist; we expressed them here just to make the data set meaningful to the reader. * Add all the ones and zeros in the record together to obtain the Hamming distance. Hamming distance = 0+1+0+1+1+1=

4.

-Update the distance matrix.

Rec1 Rec2 Rec1 0 4 Rec2 4 0

• Create a cluster with minimum distance.

• The systems read new records and the previous steps are repeated. At the end a new cluster is created.

The iteration stops at this step because the execution loop produces a single cluster and no cluster can be created any further. We discuss the implementation of IBRIDIA in the next section.

Implementation of IBRIDIA

We studied various technologies for implementing IBRIDIA. We investigated existing libraries for data extraction, filtering, and transformation. Our goal was to reuse existing ones instead of developing the new ones. Also, we studied machine learning libraries including DatumBox 6 , SPMF 7 , Massive Online Analysis (MOA), and Spark MLib 8 to implement our data processing model. From our study, we found that existing libraries could not be used to implement our model (discussed in the previous section). Therefore, we decided to implement the model on our own. For implementation, we used Java language on Eclipse.

To sum up, IBRIDIA is a framework that integrates three APIs for extracting external data from different sources including Twitter API, Facebook API, and Open Weather API. It uses an open source parser. Also, it includes tools for cleaning and transforming incoming data. The prototype of ProLoD is available in GitHub.

We investigated different data processing frameworks including Apache Spark 9 , and Apache Storm10 to develop RePloD module of IBRIDIA. To the best of our understanding Storm is more potential computation system for our ProLoD. It is fast, can process over a million of tuples per second.

It is scalable, fault-tolerant, guarantees our data will be processed, and is easy to set up and operate. Storm integrates with the queuing and database technologies we already use. A Storm topology consumes streams of data and processes those streams in arbitrarily complex ways. However, repartitioning the streams between each stage of the running computation is needed.

RePLoD consists of two main components: data streamer and data processor. We implemented the Data Streamer using Apache Kafka11 and data processing engine using Apache Storm12 .

• Apache Kafka: It is a publish-subscribe based fault tolerant messaging system. It is fast and highly scalable distributed messaging technology. It is used in building durable data collection system where high throughput and reliable delivery of messages are critically important.

Apache Kafka messaging system is merely a collection of topics split into one or more partitions. A Kafka partition is a linearly ordered sequence of messages, where each message is identified by their index (called as offset). All the data in a Kafka cluster is the disjointed union of partitions. Incoming messages are written at the end of a partition and messages are sequentially read by consumers. Durability is provided by replicating messages to different brokers. Apache Kafka provides four different types of APIs. The Producer API allows RePLoD to publish a stream of records to one or more Kafka topics.

The Consumer API allows RePLoD to subscribe to one or more topics and process the stream of records produced to them. The Streams API allows RePLoD to act as a stream processor, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams. The Connector API allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to a table. The data processing topology of RePLoD comprises the data cleaner, the data filter, the data transformer, and the data clustering component. Figure 4 shows the data processing topology of RePLoD.

The spouts and bolts RePLoD Topology constitute a directed acyclic graph (DAG). Spout is the entry point to the topology used to read the data from Apache Kafka. The Kafka-spout acts as Kafka consumer of the Kafka however, not all of them are important for the analysis. Therefore, we need to filter the relevant ones to our analysis such as "id, user, description, text, created time, location, etc". Then the simplified records that are emitted from the bolt twitterfilter are ingested as input to the next bolt twittercleaner which is used for cleaning all the characters that may affect the analysis. The analysis is carried out by the bolt real-time-clustering that is built on the real-time clustering algorithm that results in different clusters: merged, splitted or newly created. Once the data is cleaned, they are transformed the texts (e.g, tweets) to csv-like structure using "twittertransformer" bolt. After the transformation is completed, we extract different named entities to understand the text content and make the analysis able to depend upon the features mentioned in the content. Finally, the clustering is carried out in real-time using twitteranalytics bolt which is built on hierarchical clustering algorithm that produces clusters in real-time. The clusters are saved in the disk.

Experiments

In this section, we discuss the results produced through experiments with ProLoD and RePLoD. We evaluate the performance of ProLoD and RePLoD over the metric execution time. Given below is the specification of the machine we used for our experiments:

• Processor: 2.40 GHz

• Memory: 4GB

• HDD: 500 GB

• Operating System: Windows 10(64 bit)

We compare the performance of our model with the one implemented by WEKA. Although we tested the performance of SPMF and Spark, unfortunately, we could not compare them to our work since they can only be applied to numerical data. Concerning MOA, we found bugs in it and thus we could not run our model. It allows only ARFF file formats as mentioned before and even though we converted our file to the needed format, it throws multiple exceptions when we tried to read data from an external file.

We implemented two different versions of our model: real-time and Batch style. We tested both versions with a test dataset. RePLoD reads data by records and clusters each incoming record. The clusters are mutable; a cluster may change when a new record is added in the cluster. However, since the algorithm is greedy, the execution time has a positive correlation with number of records, i.e., the execution time increases as the number of records increases. This can be solved using the scalability of the system by inserting more nodes to the cluster for faster processing resources. Table 1 shows the result of our experiment with the realtime version "RePLoD". The batch-style ProLoD performs bulk reading and clusters batch data.

The reading and processing occur only once per batch. Table 2 shows the results of batch style version "ProLoD". We compared the performance of both versions. Figure 5 shows the comparison. According to our experiments, we observed that the batch-style version performs better than the real-time version of IBRIDIA. Our study reveals that performance of the real-time version was not satisfactory due to the centralized environment of the experiment consisting of only one node.

We believe that performance will improved significantly in a distributed and scalable computation framework with multiple nodes. We believe that technologies which we used for implementation of our solution called Apache Storm has high computational power; hence, it can process a huge number of records within a unit of time e.g., a millisecond. In addition, we assume that the performance of the batch-style version of ProLoD may decrease if the dataset is large. Currently, the dataset is small; therefore, the size of each batch is small and faster in processing (clustering). We compared our model with the hierarchical clustering algorithm of WEKA. The WEKA algorithm produced 7 clusters with 93 records. Table 3 shows the result which is also visualized in Figure 6 in-which the distribution of the records is also presented as percentage for each cluster separately. Furthermore, we observed that both real-time and batch style versions of ProLoD produced consistent and representable clusters that will assist in exploring data, discovering insights, and supporting predictive analytics when data distribution is observed. Table 4 shows the result which is also shown in Figure 7 in-which the distribution of the records is also presented as percentage for each cluster separately. According to our analysis, any labeling process using any of the attributes from the dataset will produce results that are representable and understandable (by representable we meant a reasonable number of clusters produced by the algorithm). Many of the clusters produced by WEKA intersect with other clusters while treated independently. For example cluster 1 and cluster 2 do not need to be represented as two different clusters and cluster 4 and cluster 7 intersect due to the fact that they have a common value called Major Event of the attribute type of event. According to our observation, the separation of clusters (if applicable) is more effective than clusters with intersection over common values such as Major Events. Some clusters are misclassified and not representable, according to our observation. In Figure 6 some clusters such as strike, sports meeting etc. do not represent meaningful result. On the other hand, in Figure 7 clusters are more representative in terms of meaningfulness. For instance, an interpretation of data over the type of event attribute will produce clusters containing similar records. Similarly, an interpretation of data over the public event type and network management type attributes will produce groups separating public events (e.g. Activities) from network management events (e.g. narrow lanes, road closed). Such simple and comprehensive visualization will make anal-ysis readable for the experts. In addition, automated systems can reap the benefits from categorizing data before applying analytics.

Discussion

In this work we considered two quality attributes regarding our solution: performance of the system and accuracy of our results that is the number of clusters produced by our algorithms. Our observation reveals that the clustering process in RePLoD (which we proposed in this paper for realtime clustering) consume a significant amount of time which was essentially unexpected. We found that the time consumption increases due to the need of continuous listening to new data being fetched. Another reason was the need for applying the clustering process all over again each time a new record is fetched in order to deal with the evolution characteristic of clusters in real time.

Furthermore, the clusters that were produced during our experiments both in RePLoD and ProLoD are better than WEKA's hierarchical clustering algorithm. The results obtained by WEKA were a set of seven clusters placing common records (e.g. cluster 1 and cluster 2) without any meaningful insight; nevertheless, the records corresponding to Road Closed are found in three different clusters. On the other hand, our clustering results showed that activities events (major event, sports meeting, and strike) were clustered together, network management events (road closed and narrow lanes) created a cluster, and traffic events (queueing traffic) created a cluster. Therefore, predictive analytics can be applied to such self-explanatory clusters instead of data points.

Conclusion

In this paper, we presented a framework called IBRIDIA for processing logistics data. These data are stemming from various internal and external sources within the logistics domain. The proposed framework integrates several data preparation and processing functions. The output of such functions is a clustered dataset. Afterward, the analytical engine exploits this dataset to perform predictive analytics. In order to develop our data processing model, we studied different machine learning algorithms; eventually, we choose Johnson's hierarchical clustering algorithm. However, we modified the algorithm to become a stream clustering algorithm that supports incremental grouping of text messages according to their similar characteristics directly on the go. We presented the solution architecture of IBRIDIA which consists of components for performing data preparation tasks such as collection, filtering, cleaning, integration, and data storage. It also includes components for processing data and applying some clustering techniques to get self-explanatory groups of data supports predictive analytics for forecasting delivery delay. Furthermore, we studied various tools to implement our algorithm. We implemented IBRIDIA by adopting both real-time and batch style computational models (ProLoD and RePLoD). Finally, we evaluated our solution with the most widely used machine learning library called WEKA. We reported and discussed the evaluation results. According to our observation, IB-RIDIA's hierarchical clustering produced better representative results. However, the real-time version's performance of ProLoD was worse than the batch style -which was due to the centralized environment of experiment consisting of only one node. Thus, we presented both ProLoD as well as our framework RePLoD for real-time collection and processing of logistics data that are stemming from sources such as social media. We tested the framework over a simple use case. Several works are planned for future work.

We plan to introduce time window to the algorithm so that we do not have to store the data more than a specific time limit in order to be processed; this effervescent data store is more suitable for realtime architectures. Using weighted distances other than binary hamming distance with the aim of representing important data point more significantly. Also, we plan to introduce the ability to deal with numerical data as categories because in this article we focused on text data whereas in the big picture numerical data is essential.

RePLoD needs to be tested over a real-world use case which will be done in near future. RePLoD will be extended in two aspects: developing a predictive analytics and a prescriptive analytics model to suggest an optimized route plan in order to prevent delay.

Figure 1 :

 1 Figure 1: The Multi-modal Logistics System (Source: [9])

Figure 2 :

 2 Figure 2: The high level architecture of IBRIDIA

Figure 3 :

 3 Figure 3: An example of cleaning data with ProLoD.

• Step 2 :• Step 3 : 1 •

 231 which allows to calculate the number of clusters that should be produced, at the end. There are several variants of the agglomerative hierarchical clustering algorithm. Below we present the steps involved in performing an agglomerative hierarchical clustering. Consider a set of data points S = (x 1 , x 2 , x 3 , ..., x n ) as input. The agglomerative hierarchical clustering algorithm performs the following steps: • Step 1 : Disjoint cluster (C) of level L(0) = 0 and sequence number M = 0 Calculate the least distance (D) pair of clusters in the current C, say pair P(r, s), according to D(r,s) = M in( D(i, j)) where the minimum is over all pairs of clusters in the current clustering Increment the sequence number, M = M + Step 4 : Merge C(r) and C(s) → C (z) which is a new cluster. Set the level of this clustering to L(z) = D (r),(s) • Step 5 : Update the distance matrix Ψ, (delete the rows and columns corresponding to clusters C (r) and C (s) and add a row and column corresponding to C (z). The distance between the new cluster, denoted (r, s) and the old cluster(k) is defined as follows: D ((k), (r, s)) = M in (D[(k),(r)], D ((k),(s))))

a

  convenient measuring mechanism for string values which covers most of the unstructured data. Hamming distance measures the minimum number of substitutions required to change one string into the other (the minimum number of errors that could have transformed one string into the other). We modified Johnson's Hierarchical Clustering algorithm to become a stream clustering algorithm that supports incremental grouping of text messages according to their similar characteristics directly on the go. The theoretical steps performed by the modified algorithm is based on the theoretical steps of any agglomerative hierarchical clustering algorithm as shown previously. We identify the practical algorithmic steps of the clustering used in our solution IBRIDIA as follows:

Figure 4 :

 4 Figure 4: The Data Processing Topology of RePLoD

Figure 5 :

 5 Figure 5: Execution time (in seconds) of RePLoD and ProLoD

Figure 6 :

 6 Figure 6: WEKA Text Clustering Results.

Figure 7 :

 7 Figure 7: RePLoD Text Clustering Results.

•

  Apache Storm: Apache Storm is a distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data for realtime processing. It is designed to process vast amount of data in a fault-tolerant and horizontal scalable method. It is a streaming data framework that has the capability of highest ingestion rates.Though Storm is stateless, its distributed environment and cluster state is managed by Apache ZooKeeper13 . It is simple and you can execute all kinds of manipulations on real-time data in parallel. Apache Storm guarantees that every message will be processed through the topology at least once.

	Apache Storm consists of four main components: tuple is the main data
	structure which is a list of ordered elements; stream is an unordered
	sequence of tuples; spouts are the sources of stream; bolts are logical
	units. Bolts can perform the operations of filtering, aggregation, join-
	ing, interacting with data sources and databases. Bolt receives data
	and emits to one or more bolts. Spouts and bolts are connected to-
	gether and they form a topology. Real-time application logic is specified
	inside Storm topology. In simple words, a topology is a directed graph
	where vertices are computation and edges are stream of data.

Table 1 :

 1 The result of an experiment with realtime version "RePLoD"

	Realtime	1st exec 2nd exec 3rd exec 4th exec 5th exec average seconds
	8 records 466	426	451	432	420	439	0.439
	16 records 3665	2301	2362	2069	2089	2497.2 2.4972
	24 records 4363	4311	4491	4386	4135	4337.2 4.3372
	32 records 7915	7926	7789	7784	7710	7824.8 7.8248
	40 records 12861	12780	12849	12969	12958	12883.4 12.8834

Table 2 :

 2 The result of an experiment with the batch style version of "ProLoD"

	Not Realtime 1st exec 2nd exec 3rd exec 4th exec 5th exec average seconds
	8 records	132	129	136	121	147	133	0.133
	16 records	262	286	257	251	286	268.4	0.2684
	24 records	465	393	427	393	429	421.4	0.4214
	32 records	555	549	617	560	535	563.2	0.5632
	40 records	728	719	779	855	726	761.4	0.7614

Table 3 :

 3 The result produced by WEKA Hierarchical Clustering algorithm

	Clusters Records
	Cluster 1	2 (2%)
	Cluster 2	8 (9%)
	Cluster 3 37 (40%)
	Cluster 4 12 (13%)
	Cluster 5	3 (3%)
	Cluster 6	4 (4%)
	Cluster 7 27 (29%)

Table 4 :

 4 The result produced by RePLoD Hierarchical Clustering algorithm

	Clusters Records and Clusters
	Cluster 1	10 (11%)
	Cluster 2	31 (33%)
	Cluster 3 Cluster 1 and Cluster 2
	Cluster 4	50 (54%)
	Cluster 5 Cluster 3 and Cluster 4
	Cluster 6	2 (2%)
	Cluster 7 Cluster 5 and Cluster 6

http://www.dhl.com/en.html

http://www.fedex.com/us/

http://moa.cms.waikato.ac.nz

http://www.cs.waikato.ac.nz/ml/weka/

We contacted Dr. Albert Bifet the author of the library for assistance because it was only running for a specific number of data points then starts throwing errors but the problem was not solved.

http://www.datumbox.com

http://www.philippe-fournier-viger.com/spmf/

http://spark.apache.org/mllib/

http://spark.apache.org

http://storm.apache.org

Apache Kafka: https://kafka.apache.org

Apache Storm: http://storm.apache.org

https://zookeeper.apache.org

• Start with each record as a cluster on its own.

• Read new data streams NetworkManagement 1/30/2017 16:47 NarrowLanes LYON-01

• A unique item is added in the vector format.

• Fill in the matrix of items absence and presence. • Build the similarity matrix using hamming distance. Currently, there is only one record.

-The algorithm reads new record.